Methods for determining the prognosis for cancer patients using TUCAN

ABSTRACT

The invention provides methods for determining a prognosis for survival for a cancer patient. One method involves (a) measuring a level of a TUCAN in a neoplastic cell-containing sample from the cancer patient, and (b) comparing the level of TUCAN in the sample to a reference level of TUCAN, wherein a low level of TUCAN in the sample correlates with increased survival of the patient. Another method involves (a) measuring a level of TUCAN in a neoplastic cell-containing sample from the cancer patient, and (b) classifying the patient as belonging to either a first or second group of patients, wherein the first group of patients having low levels of TUCAN is classified as having an increased likelihood of survival compared to the second group of patients having high levels of TUCAN.

This application is a divisional of U.S. patent application Ser. No.10/141,618, filed May 7, 2002, which is a continuation-in-part of U.S.patent application Ser. No. 09/388,221, filed Sep. 1, 1999, and claimsthe benefit of U.S. Provisional Application No. 60/356,934, filed Feb.12, 2002, and U.S. Provisional Application No. 60/289,233, filed May 7,2001, each of which is incorporated herein by reference.

This invention was made with government support under grant numbersAG15402, CA69381 and NS36821, awarded by the National Institutes ofHealth. The United States Government has certain rights in thisinvention.

BACKGROUND OF THE INVENTION

This invention relates generally to cancer and, more specifically, tobiomarkers that can be used to diagnose or prognose cancer.

Cancer remains a major public health problem that profoundly affects themore than 1 million people diagnosed each year, as well as theirfamilies and friends. As our Nation's population grows and ages, morepeople will get cancer. The use of screening tests to detect cancersearly often leads to more effective treatment with fewer side effects.Patients whose cancers are found early also are less likely to die fromthese cancers than are those whose cancers are not found until symptomsappear.

One type of cancer screening test involves the detection of a biomarker,such as a tumor marker, in a fluid or tissue obtained from a patient.Tumor markers are substances produced by cancer cells that are nottypically produced by normal cells. These substances generally can bedetected in the body fluids or tissues of patients with cancer.Unfortunately, some tumor markers also can be detected in significantamounts in the body fluids or tissues of people who do not have cancer,making certain markers less reliable for diagnosis. Nevertheless, tumormarkers remain an important tool for diagnosing cancer.

Another important use for tumor markers is for monitoring patients beingtreated for advanced cancer. Measuring tumor markers for this purposecan be less invasive, less time-consuming, as well as less expensive,than repeating chest x-rays, computed tomography (CT) scans, bone scans,or other complicated tests, to determine if a therapy is reducing thecancer.

A further important use for tumor markers is for determining a prognosisof survival of a cancer patient. Such prognostic methods can be used toidentify surgically treated patients likely to experience cancerrecurrence so that they can be offered additional therapeutic options.Biomarkers useful for prognosis of survival also can be especiallyeffective for determining the risk of metastasis in patients whodemonstrate no measurable metastasis at the time of examination orsurgery. Knowledge of the likelihood of metastasis in a cancer patientcan be an important factor in selecting a treatment option. For example,a cancer patient likely to experience metastasis may be advantageouslytreated using a modality that is particularly aggressive.

Thus, there exists a need for identification of biomarkers that can beused as diagnostic and prognostic indicators for cancer. The presentinvention satisfies this need and provides related advantages as well.

SUMMARY OF THE INVENTION

The invention provides methods for determining a prognosis for survivalfor a cancer patient. One method involves (a) measuring a level of aTUCAN in a neoplastic cell-containing sample from the cancer patient,and (b) comparing the level of TUCAN in the sample to a reference levelof TUCAN, wherein a lower level of TUCAN in the sample correlates withincreased survival of the patient.

Another method for determining a prognosis for survival for a cancerpatient involves (a) measuring levels of TUCAN and one or morebiomarkers selected from the group consisting of cIAP2, Apaf1, Bcl-2 andSmac in a neoplastic cell-containing sample from the cancer patient, and(b) comparing the level of TUCAN and the one or more selected biomarkersin the sample to a reference level of TUCAN and the one or more selectedbiomarkers, wherein a low level of TUCAN and a high level of any ofApaf1, Bcl-2 or Smac, or a low level of TUCAN and a low level of cIAP2,in said sample correlate with increased survival of said patient.

A further method of determining a prognosis for survival for a cancerpatient involves (a) measuring a level of TUCAN in a neoplasticcell-containing sample from the cancer patient, and (b) classifying thepatient as belonging to either a first or second group of patients,wherein the first group of patients having low levels of TUCAN isclassified as having an increased likelihood of survival than the secondgroup of patients having high levels of TUCAN.

The invention also provides a method for monitoring the effectiveness ofa course of treatment for a patient with cancer. The method involves (a)determining a level of a TUCAN in a neoplastic cell-containing samplefrom the cancer patient prior to treatment, and (b) determining thelevel of TUCAN in a neoplastic cell-containing sample from the patientafter treatment, whereby comparison of the TUCAN level prior totreatment with the TUCAN level after treatment indicates theeffectiveness of the treatment.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1F show examples of immunohistochemical detection of IAP-familyproteins, Apaf1 and TUCAN in normal and malignant colon tissues. FIG. 1Ashows a colon cancer microarray slide stained for cIAP2 (×5magnification). Examples of normal colonic epithelium immunostaining arepresented for cIAP1 (FIG. 1B; ×100), Survivin (FIG. 1D; ×150), Smac(FIG. 1E; ×150), AIF (FIG. 1G; ×150), and Tucan (FIG. 1K; ×20).Immunostaining results in regions of invasive cancer are shown for Smac(FIG. 1F; ×400), AIF (FIG. 1H; ×250), Apaf1 (FIG. 1I, J; ×200), TUCAN(FIG. 1L ×20; M ×400), and Bcl-2 (FIG. 1N; ×150). Examples of malignantand the adjacent normal colonic epithelium are presented for cIAP2 (FIG.1C; ×40), p53 (FIG. 10; ×150) and MIB-1 (FIG. 1P; ×400).

FIGS. 2A and 2B show comparisons of immunoscores for normal andmalignant colon tissues.

FIGS. 3A-3F show the specificity of antibodies and expression of IAPs,Apaf1 and TUCAN protein in colon carcinoma by immunoblotting. FIG. 3Ashows immunoblot analysis of the indicated in vitro translated proteinsusing polyclonal XIAP antiserum. FIG. 3B shows immunoblot analysis ofrecombinant IAP-family proteins and lysates from normal tissues lackingSurvivin mRNA and protein versus tumor cell lines which express Survivinprotein using anti-Survivin antiserum. FIG. 3C shows immunoblot analysisof GST-Smac recombinant protein using anti-Smac antiserum. FIG. 3D showsimmunoblot analysis of Jurkat cells transfected as indicated using antiAIF antiserum. FIG. 3E shows immunoblot analysis of detergent lysates offive frozen colon cancer specimens which were identified as havingsufficient amounts of both adjacent normal (N) and tumor (T) tissue forimmunoblot analysis using antibodies specific for IAPs, Apaf1, and otherproteins. FIG. 3F shows densitometry analysis of the immunoblots shownin FIG. 3E.

FIG. 4 shows correlations of biomarker immunostaining data withdisease-free (left panels, labeled “DFS”) and overall survival (rightpanels, labeled “OS”) for colon carcinoma patients.

FIGS. 5A-5D show correlations of biomarkers and their combinations withdisease-free (FIGS. 5A and 5B) and overall (FIGS. 5C and 5D) survivalfor colon carcinoma patients. FIGS. 5A and 5B illustrate a combinationof biomarkers (FIG. 5A, low cIAP2 and high Apaf1; FIG. 5B, low cIAP2 andlow TUCAN) with positive impact on disease-free survival. The twocombinations of markers with an adverse effect on survival are presentedin FIG. 5C (low Apaf1 and high TUCAN) and FIG. 5D (low Bcl-2 and highcIAP2).

FIGS. 6A and 6B show expression of TUCAN in several tumor cell lines.FIG. 6A shows immunoblot analysis of representative human tumor celllines from the NCI panel of 60 human tumor cell lines using an antiserumspecific for TUCAN. FIG. 6B shows immunoblot analysis using TUCANantiserum showing the levels of endogenous TUCAN protein in some ofthese cancer cell lines compared with HEK293T and Jurkat cellstransfected with TUCAN.

FIGS. 7A-7G show immunohistochemical analysis of TUCAN expression incolorectal cancer.

FIGS. 8A-8C show that TUCAN binds selectively to pro-caspase-9 and toitself. FIGS. 8A-8C show representative results fromco-immunoprecipitation experiments performed using TUCAN containingeither Flag or Myc epitope tags. FIG. 8A shows that TUCANco-immunoprecipitated with pro-caspase-9 but not the CARD-containingprotein Apaf1. TUCAN also did not associate non-specifically withpro-caspase-8 and -10. FIG. 8B shows that pro-caspase-9co-immunoprecipitated with full-length TUCAN and the CARD only fragmentof TUCAN (residues 345-431) but not the ΔCARD fragment of TUCAN lackingthe CARD (residues 1-337). FIG. 8C shows self-association of TUCAN usingHA and Myc-tagged proteins. Full-length TUCAN interacted withfull-length TUCAN and the CARD-only fragment but not the ΔCARD fragment.

DETAILED DESCRIPTION OF THE INVENTION

This invention relates to the finding that expression of the CARD domaincontaining protein, TUCAN (Tumor Up-regulated CARD-containing Antagonistof Caspase Nine), formerly known as CARD-X in PCT publication WO01/16170, can be used to effectively predict clinical outcome forpatients with cancer, either independently, or in combination with otherbiomarkers.

The prognostic methods of the invention are useful for determining if apatient is at risk for relapse. Cancer relapse is a concern relating toa variety of types of cancer. For example, of patients undergoingcomplete surgical removal of colon cancer, 25-40% of patients with stageII colon carcinoma and about 50% of patients with stage III coloncarcinoma experience cancer recurrence.

One explanation for cancer recurrence is that patients with relativelyearly stage disease (for example, stage II or stage III) already havesmall amounts of cancer spread outside the affected organ that were notremoved by surgery. These cancer cells, referred to as micrometastases,cannot typically be detected with currently available tests. Theprognostic methods of the invention can be used to identify surgicallytreated patients likely to experience cancer recurrence so that they canbe offered additional therapeutic options, including preoperative orpostoperative adjuncts such as chemotherapy, radiation, biologicalmodifiers and other suitable therapies. The methods are especiallyeffective for determining the risk of metastasis in patients whodemonstrate no measurable metastasis at the time of examination orsurgery.

The prognostic methods of the invention also are useful for determininga proper course of treatment for a patient having cancer. A course oftreatment refers to the therapeutic measures taken for a patient afterdiagnosis or after treatment for cancer. For example, a determination ofthe likelihood for cancer recurrence, spread, or patient survival, canassist in determining whether a more conservative or more radicalapproach to therapy should be taken, or whether treatment modalitiesshould be combined. For example, when cancer recurrence is likely, itcan be advantageous to precede or follow surgical treatment withchemotherapy, radiation, immunotherapy, biological modifier therapy,gene therapy, vaccines, and the like, or adjust the span of time duringwhich the patient is treated.

As disclosed herein in Examples II and VIII, elevated levels of TUCANwere found in 49 and 64% of colon tumor specimens examined,respectively. Univariate analysis was used to determine significantcorrelations between longer disease-free survival (DFS) and lowexpression of TUCAN (p=0.0004). As shown in Example IV, 78% (39/50) ofpatients whose tumors contained low levels of TUCAN remained alive anddisease-free during the time covered by this study, compared to only 44%(21/48) of those with high expression of this protein. Example VIII alsoindicates that TUCAN immunostaining was significantly higher amongpatients who died of colon cancer, as compared to patients who remainedalive.

As shown in Example IV, at a median follow-up of 5 years, 49% ofpatients with high expression of TUCAN had relapse or died of coloncancer, and only 19% had recurrence and 4% died of disease in a group ofpatients whose tumors expressed low levels of this protein. Multivariateanalysis indicated that the presence of high TUCAN increased risk ofdeath from colon cancer 17-fold (p=000004). Therefore, a high level ofTUCAN in a sample from a patient with cancer correlates with increasedlikelihood of tumor metastasis and reduced survival. Similarly, a lowlevel of TUCAN in a sample from a patient with cancer correlates withdecreased likelihood of tumor metastasis and increased likelihood ofsurvival.

Also disclosed herein is the observation that the combination of lowlevels of cIAP2 and low levels of TUCAN identified a subgroup ofearly-stage colon cancer patients with very favorable outcome.Approximately one-third of patients in a cohort of 92 patients had acombination of low cIAP2 and low TUCAN (33/92 [36%]). Among these 33patients, 32 (97%) remained alive and 30 (91%) disease-free during thetime covered by this study, as opposed to 56% and 44% for othercategories of patients. Similarly, in a cohort of 81 patients, 17 had acombination of high Apaf1 and low TUCAN. All (17) patients featuringhigh expression of Apaf1 and low TUCAN were alive and relapse-free atthe end of the survey, compared to only 65% (53/81) alive and 53%(43/81) recurrence-free for those who were not characterized by thisfeature. Therefore, a high level of TUCAN combined with a high level ofcIAP2 or a low level of Apaf1 in a sample from a patient with cancercorrelates with increased likelihood of tumor metastasis and reducedlikelihood of survival, whereas a low level of TUCAN combined with a lowlevel of cIAP2 or a high level of Apaf1 in a sample from a patient withcancer correlates with reduced likelihood of tumor metastasis andincreased likelihood of survival.

Based on these results, the invention provides methods for diagnosingneoplastic conditions, prognosing survival of patients suffering fromcancer, and determining a stage of cancer using TUCAN as a biomarker.TUCAN can be used alone or in combination with other prognosticindicators as a specific biomarker for prognosing survival of patientssuffering from cancer.

As disclosed herein, elevated levels of

Apaf1, Survivin, XIAP, cIAP1, and cIAP2 were found in 38%, 54%, 74%, 61%and 35% of colon tumor specimens, respectively. Univariate analysis wasused to determine significant correlations between longer disease-freesurvival (DFS) and low expression of cIAP2 (p=0.0002), β-Catenin(p=0.04), mutant p53 protein (p=0.03), or high levels of Apaf1(p=0.00008), Bcl-2 (p=0.005), and SMAC (p=0.03) (see FIG. 4 a). Thus,78% (39/50) of patients whose tumors contained low levels of TUCANremained alive and disease-free during the time covered by this study,compared to only 44% (21/48) of those with high expression of thisprotein. Similarly, 74% (45/61) of low cIAP2 expressors were cancer-freeat the time of last survey compared to only 36% (12/33) of those withhigh cIAP2 levels. At a median follow-up of 5 years, 60% of patientswith high cIAP2 levels relapsed and 46% died of colon cancer, whereas ina low-cIAP2 group there were 20% relapses and 18% colon cancer-relateddeaths.

As further disclosed herein, high levels of Apaf1 were associated withlonger survival, with 33/38 (87%) of colon cancer patients remainingdisease-free compared to only 28/62 (45%) of those with low Apaf1expression. In contrast, 43% of patients with low Apaf1 relapsed and 35%died of colon cancer, while only 14% had a cancer recurrence or died ina high-Apaf1 cohort. Low Bcl-2 levels also were associated with pooroverall survival. Of 18 patients with low expression of this protein, 11(61%) died of colon cancer, compared with 24% of patients who died inthe high-Bcl-2 group (18/76). Similarly, patients whose tumors containedlow Apaf1 staining had worse overall survival compared with those whooverexpressed Bcl-2 (FIG. 1N). Multivariate analysis indicated that highApaf1 and Bcl-2 expression was associated with a decreased relative riskof dying of colon cancer by 75% (p=0.004) and 82% (p=0.00006).Therefore, a decreased level of Apaf1 or Bcl-2 in a sample from apatient with colon cancer correlates positively with increased chance oftumor metastasis and reduced survival.

Also disclosed herein is the observation that the combination of lowlevels of cIAP2 and high levels of Apaf1 identified a subgroup ofearly-stage colon cancer patients with very favorable outcome. Roughlyone-quarter (25/94 [27%]) of the tumors analyzed contained both lowcIAP2 and high Apaf1. Among these 25 patients, all 25 remained alive andfree of disease after surgery at the time of last survey (medianfollow-up 5 years). Thus, the median 5 yr disease-free and overallsurvival rate for this group of patients was 100%, compared to only 50%and 64% for other categories of patients, respectively. Therefore, anincreased level of cIAP2 and decreased level of Apaf1 in a sample from apatient with colon cancer correlates with increased chance of tumormetastasis and reduced survival.

As used herein, the term “level” refers to mean the amount, accumulationor rate of a biomarker molecule, such as TUCAN. A level can berepresented, for example, by the amount or synthesis rate of messengerRNA (mRNA) encoded by a gene, the amount or synthesis rate ofpolypeptide corresponding to a given amino acid sequence encoded by agene, or the amount or synthesis rate of a biochemical form of amolecule accumulated in a cell, including, for example, the amount ofparticular post-synthetic modifications of a molecule such as apolypeptide, nucleic acid or small molecule. The term can be used torefer to an absolute amount of a molecule in a sample or to a relativeamount of the molecule, including amounts determined under steady-stateor non-steady-state conditions. The expression level of a molecule canbe determined relative to a control molecule in a sample.

When used in reference to TUCAN mRNA or polypeptide, the term levelrefers to the extent, amount or rate of synthesis of the nucleic acidsequence shown as SEQ ID NO:1 or the TUCAN polypeptide shown as SEQ IDNO:2, or substantially the same nucleotide or amino acid sequences. Thenucleic acid sequence and amino acid sequence of TUCAN, formerlyreferenced as CARD-X, are also described in PCT publication WO 01/16170,which is incorporated herein by reference. When used in reference tocIAP2 mRNA or polypeptide expression, the term level refers to theextent, amount or rate of synthesis of the nucleic acid sequence shownas SEQ ID NO:5 or the CIAP2 polypeptide shown as SEQ ID NO:6, orsubstantially the same nucleotide or amino acid sequences. When used inreference to β-catenin mRNA or polypeptide, the term level refers to theextent, amount or rate of synthesis of the nucleic acid sequence shownas SEQ ID NO:7 or the β-catenin polypeptide shown as SEQ ID NO:8, orsubstantially the same nucleotide or amino acid sequences. When used inreference to Apaf1 mRNA or polypeptide, the term level refers to theextent, amount or rate of synthesis of the nucleic acid sequence shownas SEQ ID NO:9 or the Apaf1 polypeptide shown as SEQ ID NO:10, orsubstantially the same nucleotide or amino acid sequences. When used inreference to Bcl-2 mRNA or polypeptide, the term level refers to theextent, amount or rate of synthesis of the nucleic acid sequence shownas SEQ ID NO:11 or the Bcl-2 polypeptide shown as SEQ ID NO:12, orsubstantially the same nucleotide or amino acid sequences. When used inreference to Smac mRNA or polypeptide, the term level refers to theextent, amount or rate of synthesis of the nucleic acid sequence shownas SEQ ID NO:13 or the Smac polypeptide shown as SEQ ID NO:14, orsubstantially the same nucleotide or amino acid sequences. A level ofthese and other biomarkers of cancer, including XIAP, cIAP1, Survivin,Bcl-XL, Bax, BAG1, mutant p53, p53 and MIB-1, can be a gene expressionlevel or a polypeptide expression level.

An amino acid sequence that has substantially the same amino acidsequence as a reference amino acid sequence contains a considerabledegree of sequence identity or similarity, such as at least 70%, 80%,got, 95%, 98%, or 100% sequence identity or similarity, to a referenceamino acid sequence. Such changes, gaps and insertions can be naturallyoccurring mutations, or can result from processing a sample containingthe polypeptide. A nucleotide sequence that is substantially the same asa reference nucleotide sequences contains a considerable degree ofsequence identity or similarity, such as at least 70%, 80%, 90%, 95%,98%, or 100% sequence identity or similarity, to the referencenucleotide sequence. Such differences can be due to genetic differencesbetween individuals, such as mutations and polymorphisms of a gene.Differences between nucleotide and amino acid sequences can bedetermined using available algorithms and programs such as theSmith-Waterman algorithm and the BLAST homology search program (Altschulet al., J. Mol. Biol. 215:403-410 (1990)).

A gene expression level of a molecule is intended to mean the amount,accumulation or rate of synthesis of a biomarker gene. The geneexpression level can be represented by, for example, the amount ortranscription rate of hnRNA or mRNA encoded by a gene. A gene expressionlevel similarly refers to an absolute or relative amount or a synthesisrate determined, for example, under steady-state or non-steady-stateconditions.

A polypeptide expression level is intended to mean the amount,accumulation or rate of synthesis of a biomarker polypeptide. Thepolypeptide expression level can be represented by, for example, theamount or rate of synthesis of the polypeptide, a precursor form or apost-translationally modified form of the polypeptide. Variousbiochemical forms of a polypeptide resulting from post-syntheticmodifications can be present in cell contained in a sample. Suchmodifications include post-translational modifications, proteolysis, andformation of macromolecular complexes. Post-translational modificationsof polypeptides include, for example, phosphorylation, lipidation,prenylation, sulfation, hydroxylation, acetylation, addition ofcarbohydrate, addition of prosthetic groups or cofactors, formation ofdisulfide bonds and the like. In addition, it is understood thatfragments of a polypeptide are included within the definition of apolypeptide expression level. Fragments can include, for example, aminoterminal, carboxyl terminal, or internal deletions of a full lengthpolypeptide. Accumulation or synthesis rate with or without suchmodifications is included with in the meaning of the term. Similarly, apolypeptide expression level also refers to an absolute amount or asynthesis rate of the polypeptide determined, for example, understeady-state or non-steady-state conditions.

As used herein, the term “reference level” refers to a control level ofexpression of a biomarker used to evaluate a test level of expression ofa biomarker in a neoplastic cell-containing sample of a patient. Forexample, when the level of TUCAN in the neoplastic cells of a patientare higher than the reference level of TUCAN, the cells will beconsidered to have a high level of expression, or overproduction, ofTUCAN. Conversely, when the level of TUCAN in the neoplastic cells of apatient are lower than the reference level, the cells will be consideredto have a low level of expression, or underproduction, of TUCAN.

The reference level can be determined by a plurality of methods,provided that the resulting reference level accurately provides a levelof a biomarker above which exists a first group of patients having adifferent probability of survival than that of a second group ofpatients having levels of the biomarker below the reference level. Thereference level can be determined by, for example, measuring the levelof expression of a biomarker in non-tumorous cancer cells from the sametissue as the tissue of the neoplastic cells to be tested. The referencelevel can also be a level of a biomarker of in vitro cultured cellswhich can be manipulated to simulate tumor cells, or can be manipulatedin any other manner which yields expression levels which accuratelydetermine the reference level.

The reference level can also be determined by comparison of the level ofa biomarker, such as TUCAN, in populations of patients having the samecancer. This can be accomplished, for example, by histogram analysis, inwhich an entire cohort of patients are graphically presented, wherein afirst axis represents the level of the biomarker, and a second axisrepresents the number of patients in the cohort whose neoplastic cellsexpress the biomarker at a given level. Two or more separate groups ofpatients can be determined by identification of subsets populations ofthe cohort which have the same or similar levels of the biomarker.Determination of the reference level can then be made based on a levelwhich best distinguishes these separate groups. A reference level alsocan represent the levels of two or more markers. Two or more markers canbe represented, for example, by a ratio of values for levels of eachbiomarker.

The reference level can be a single number, equally applicable to everypatient, or the reference level can vary, according to specificsubpopulations of patients. For example, older men might have adifferent reference level than younger men for the same cancer, andwomen might have a different reference level than men for the samecancer. Furthermore, the reference level can be some level determinedfor each patient individually. For example, the reference level might bea certain ratio of a biomarker in the neoplastic cells of a patientrelative to the biomarker levels in non-tumor cells within the samepatient. Thus the reference level for each patient can be proscribed bya reference ratio of one or more biomarkers, such as TUCAN, wherein thereference ratio can be determined by any of the methods for determiningthe reference levels described herein.

As used herein, the term “neoplastic cell” refers to any cell that istransformed such that it proliferates without normal homeostatic growthcontrol. Such cells can result in a benign or malignant lesion ofproliferating cells. Such a lesion can be located in a variety oftissues and organs of the body. Table 1, below, provides a list ofexemplary types of cancers from which a neoplastic cell can be derived.

As used herein, the term “cancer” is intended to mean a class ofdiseases characterized by the uncontrolled growth of aberrant cells,including all known cancers, and neoplastic conditions, whethercharacterized as malignant, benign, soft tissue or solid tumor. Specificcancers include digestive and gastrointestinal cancers, such as analcancer, bile duct cancer, gastrointestinal carcinoid tumor, coloncancer, esophageal cancer, gallbladder cancer, liver cancer, pancreaticcancer, rectal cancer, appendix cancer, small intestine cancer andstomach (gastric) cancer; breast cancer; ovarian cancer; lung cancer;renal cancer; CNS cancer; leukemia and melanoma. By exemplification, alist of known cancers is provided below in Table 1. TABLE 1 Types ofCancer HEMATOPORETIC NEOPLASMS Lymphoid Neoplasms Myeloid NeoplasmsHistiocytoses Precursor B lymphoblastic leukemia/lymphoma (ALL)Precursor T lymphoblastic leukemia/lymphoma (ALL) Chronic lymphocyticleukemia/small lymphocytic lymphoma (SLL) Lymphoplasmacytic lymphomaMantle cell lymphoma Follicular lymphoma Marginal zone lymphoma Hairycell leukemia Plasmacytoma/plasma cell myeloma Diffuse large B-celllymphoma Burkitt lymphoma T-cell chronic lymphocytic leukemia Largegranular lymphocytic leukemia Mycosis fungoids and sezary syndromePeripheral T-cell lymphoma, unspecified Angioimmunoblastic T-celllymphoma Angiocentric lymphoma (NK/T-cell lymphoma) Intestinal T-celllymphoma Adult T-cell leukemia/lymphoma Anaplastic large cell lymphomaHodgkin Diseases (HD) Acute myclogenous leukemia (AML) Myclodysplasticsyndromes Chronic Myclofroliferative Disorders Chronic MyclogenousLeukemia (CML) Polycythemia Vera Essential Thrombocytosis Myelofibrosiswith Myeloid Metaplasia Hemangioma Lymphangioma Glomangioma KaposiSarcoma Hemanioendothelioma Angiosarcoma Hemangiopericytoma HEAD & NECKBasal Cell Carcinoma Squamous Cell Carcinoma Ceruminoma OsteomaNonchromaffin Paraganglioma Acoustic Neurinoma Adenoid Cystic CarcinomaMucoepidermoid Carcinoma Malignant Mixed Tumors Adenocarcinoma LymphomaFibrosarcoma Osteosarcoma Chondrosarcoma Melanoma OlfactoryNeuroblastoma Isolated Plasmocytoma Inverted Papillomas UndifferentiatedCarcinoma Mucoepidermoid Carcinoma Acinic Cell Carcinoma Malignant MixedTumor Other Carcinomas Amenoblastoma Odontoma THYMUS Malignant ThymomaType I (Invasive thymoma) Type II (Thymic carcinoma) Squamous cellcarcinoma Lymph epithelioma THE LUNG Squamous Cell CarcinomaAdenocarcinoma Bronchial derived Acinar; papillary; solidBronchioalveolar Small Cell Carcinoma Oat Cell Intermediate Cell LargeCell Carcinoma Undifferentiated; giant cell; clear cell MalignantMesothelioma Sarcomotoid Type Epithelial Type THE GASTROINTESTINAL TRACTSquamous Cell Carcinoma Adenocarcinoma Carcinoid Malignant MelanomaAdenocarcinoma Gastric Carcinoma Gastric Lymphoma Gastric Stromal CellTumors Lymphoma Kaposi's Sarcoma Intestinal Stromal Cell TumorsCarcinids Malignant Mesethelioma Non-mucin producing adenocarcinoma THELIVER AND THE BILIARY TRACT Hepatocellular Carcinoma CholangiocarcinomaHepatoblastoma Angiosarcoma Fibrolameller Carcinoma Carcinoma of theGallbladder Adenocarcinoma Squamous Cell Carcinoma Papillary, poorlydifferentiated THE PANCREAS Adenocarcinoma Cystadenocarcinoma InsulinomaGastrinoma Glucagonamoa THE KIDNEY Renal Cell Carcinoma Nephroblastoma(Wilm's Tumor) THE LOWER URINARY TRACT Urothelial Tumors Squamous CellCarcinoma Mixed Carcinoma Adenocarcinoma Small Cell Carcinoma SarcomaTHE MALE GENITAL TRACT Squamous Cell CarcinomaSarcinoma SperetocyticSarcinoma Embyonal Carcinoma Choriocarcinoma Teratoma Leydig Cell TumorSertoli Cell Tumor Lymphoma Adenocarcinoma Undifferentiated ProstaticCarcinoma Ductal Transitional Carcinoma THE FEMALE GENITAL TRACTSquamous Cell Carcinoma Basal Cell Carcinoma Melanoma FibrosarcomaIntaepithelial Carcinoma Adenocarcinoma Embryonal Rhabdomysarcoma LargeCell Carcinoma Neuroendocrine or Oat Cell Carcinoma AdenocarcinomaAdenosquamous Carcinoma Undifferentiated Carcinoma CarcinomaAdenoacanthoma Sarcoma Carcinosarcoma Leiomyosarcoma Endometrial StromalSarcoma Serous Cystadenocarcinoma Mucinous CystadenocarcinomaEndometrioid Tumors Adenosarcoma Celioblastoma (Brenner Tumor) ClearCell Carcinoma Unclassified Carcinoma Granulosa-Theca Cell TumorSertoli-Leydig Cell Tumor Disgerminoma Teratoma THE BREAST PhyllodesTumor Sarcoma Paget's Disease Carcinoma Insitu Carcinoma InvasiveCarcinoma THE ENDOCRINE SYSTEM Adenoma Carcinoma MeningnomaCramiopharlingioma Papillary Carcinoma Follicular Carcinoma MedullaryCarcinoma Anoplastic Carcinoma Adenoma Carcinoma PheochromocytomaNeuroblastome Paraganglioma Pineal Pineoblastoma Pineocytoma THE SKINMelanoma Squamous cell carcinoma Basal cell carcinoma Merkel cellcarcinoma Extramamary Paget's Disease Paget's Disease of the nippleKaposi's Sarcoma Cutaneous T-cell lymphoma BONES, JOINTS, AND SOFTTISSUE TUMORS Multiple Myeloma Malignant Lymphoma ChondrosacrcomaMesenchymal Chondrosarcoma Osteosarcoma Ewing Tumor (Ewing Sarcoma)Malignant Giant Cell Tumor Adamantinoma Malignant Fibrous HistiocytomaDesmoplastc Fibroma Fibrosarcoma Chordoma HemangioendotheliomaMemangispericytoma Liposarcoma Malignant Fibrous HistiocytomaRhabdomysarcoms Leiomyosarcoma Angiosarcoma NERVOUS SYSTEM SchwannomaNeurofibroma Malignant Periferal Nerve Sheath Tumor AstrocytomaFibrillary Astrocytoma Glioblastoma Multiforme Brain Stem GliomaPilocytic Astrocytoma Pleomorphic Xanthorstrocytoma OligodendrogliomaEpendymoma Gangliocytoma Cerebral Neuroblastoma Central NeurocytomaDysembryoplastic Neuroepithelial Tumor Medulloblastoma MalignantMeningioma Primary Brain Lymphoma Primary Brain Germ Cell Tumor THE EYECarcinoma Squamous Cell Carcinoma Mucoepidermoid Carcinoma MelanomaRetinoblastoma Glioma Meningioma THE HEART Myxoma Fibroma LipomaPapillary Fibroelastoma Rhasdoyoma Angiosarcoma Other SarcomaHISTIOCYTOSES Langerhans Cell Histiocytosis

As used herein, the term “specifically reactive” when used in referenceto an antibody refers to the discriminatory binding of the antibody tothe indicated target polypeptide. For such binding to be discriminating,the antibody will not substantially cross react with other polypeptides.Specific reactivity can include binding properties such as bindingspecificity, binding affinity and binding avidity. For example, anantibody can bind a target polypeptide with a binding affinity (Kd) ofabout 10−⁴ M or more, 10−⁶ M or more, 10−⁷ M or more, 10−⁸ M or more,10−⁹ M or more, or 10−¹⁰ M or more. Several methods for detecting ormeasuring antibody binding are known in the art and disclosed herein.

As used herein, the term “sample” is intended to mean any biologicalfluid, cell, tissue, organ or portion thereof, that includes orpotentially includes a neoplastic cell, such as a cell from the colon,rectum, breast, ovary, prostate, kidney, lung, blood, brain or otherorgan or tissue that contains or is suspected to contain a neoplasticcell. The term includes samples present in an individual as well assamples obtained or derived from the individual. For example, a samplecan be a histologic section of a specimen obtained by biopsy, or cellsthat are placed in or adapted to tissue culture. A sample further can bea subcellular fraction or extract, or a crude or substantially purenucleic acid molecule or protein preparation.

As used herein, the term “disease-free survival” refers to the lack oftumor recurrence and/or spread and the fate of a patient afterdiagnosis, for example, a patient who is alive without tumor recurrence.The phrase “overall survival” refers to the fate of the patient afterdiagnosis, regardless of whether the patient has a recurrence of thetumor.

As used herein, the term “risk of recurrence” refers to the probabilityof tumor recurrence or spread in a patient subsequent to diagnosis ofcancer, wherein the probability is determined according to the processof the invention.

Tumor recurrence refers to further growth of neoplastic or cancerouscells after diagnosis of cancer. Particularly, recurrence can occur whenfurther cancerous cell growth occurs in the cancerous tissue. Tumorspread refers to dissemination of cancer cells into local or distanttissues and organs, for example during tumor metastasis. Tumorrecurrence, in particular, metastasis, is a significant cause ofmortality among patients who have undergone surgical treatment forcancer. Therefore, tumor recurrence or spread is correlated withdisease-free and overall patient survival.

The invention relates to the use of TUCAN as a biomarker for prognosingsurvival and monitoring the effectiveness of a treatment for a cancerpatient. TUCAN is a CARD domain-containing protein that has a role inregulating apoptosis. Apoptosis is a physiologic process that ensureshomeostasis is maintained between cell production and cell turnover inessentially all self-renewing tissues. In addition to maintaining tissuehomeostasis, apoptosis also occurs in response to a variety of externalstimuli, including growth factor deprivation, alterations in calciumlevels, free-radicals, cytotoxic lymphokines, infection by some viruses,radiation and most chemotherapeutic agents. Thus, apoptosis is aninducible event that likely is subject to similar mechanisms ofregulation as occur, for example, in a metabolic pathway. In thisregard, dysregulation of apoptosis also can occur and is observed, forexample, in some types of cancer cells, which survive for a longer timethan corresponding normal cells, and in neurodegenerative diseases whereneurons die prematurely. In viral infections, induction of apoptosis canfigure prominently in the pathophysiology of the disease process,because immune-based eradication of viral infections depends onelimination of virus-producing host cells by immune cell attackresulting in apoptosis.

The principal effectors of apoptosis are a family of intracellularproteases known as Caspases, representing an abbreviation for CysteineAspartyl Proteases. Caspases are found as inactive zymogens inessentially all animal cells. During apoptosis, the caspases areactivated by proteolytic processing at specific aspartic acid residues,resulting in the production of subunits that assemble into an activeprotease typically consisting of a heterotetramer containing two largeand two small subunits (Thornberry and Lazebnik, Science 281:1312-1316(1998)). The phenomenon of apoptosis is produced directly or indirectlyby the activation of caspases in cells, resulting in the proteolyticcleavage of specific substrate proteins.

TUCAN contains at least two protein domains, one of which is a CARD(Caspase-Associated Recruitment Domain). CARDs are protein interactionmotifs found in the N-terminal prodomains of several caspases and inapoptosis-regulatory proteins that either activate or suppressactivation of CARD-containing pro-caspases. In mammals, eightCARD-carrying caspases have been identified, including pro-caspases-1,2, 4, 5, 9, 11, 12 and 13. To date, multiple non-caspase CARD-containingproteins have been discovered and functionally characterized, includingApaf1, Nod1 (CARD4), NAC (DEPCAP), Raidd (CRADD), Cardiak (Rip2, RICK),BcllO (CIPER), ARC (Nop30), Asc, CARD9, CARD10, CARD11, CARD14, cIAP1,cIAP2, and CLAN. The CARD domains of many of these proteins are capableof binding the CARD-containing prodomains of specific CARD-carryingcaspases, either facilitating or inhibiting protease activation.

The CARD domain of TUCAN selectively binds to its own CARD and topro-Caspase-9 (see Example IX). In addition, the binding of TUCAN topro-caspase-9 has been shown to interfere with the ability ofpro-caspase-9 to interact with Apaf1. By inhibiting the interactionbetween pro-caspase-9 and Apaf1, TUCAN inhibits apoptosis signaling inthe mitochondrial/cytochrome c pathway. Consistent with this observationis that finding that over-expression of TUCAN reduces apoptosis inducedby stimuli that are known to activate the mitochondrial pathway forcaspase-activation, including Bax, DNA-damaging drugs, andstaurosporine. In contrast, apoptosis induced via alternative pathways,including GraB and Fas (TNF-family death receptor), is not inhibited byTUCAN. Further, over-expression of TUCAN in cells by stable or transienttransfection inhibits apoptosis and caspase activation induced byApaf1/caspase-9-dependent stimuli, including Bax, VP16, andStaurosporine, but not by Apaf1/caspase-9-independent stimuli, Fas andGranzyme B. These cellular functions of TUCAN indicate that it has animportant role in inhibiting mitochondrial signaling pathway-inducedapoptosis.

TUCAN also contains an N-terminal domain that shares amino-acidsimilarity with a segment of the NAC protein, a CARD-carrying regulatorof the Apaf1 apoptosome Chu et al. J Biol Chem 276:9239-9245 (2001) andHlaing et al. J Biol Chem 276:9230-9238 (2001)). The TUCAN N-terminaldomain contains several candidate phosphorylation sites, including PKC(S/T-x-R/K) sites at amino-acids 72, 286, 313, and 416, Casein kinase II(S/T-x-D/E) sites at 289, 376, 398, 414, and 416 and MAP kinase/CDK(S/T-P) sites at 187 and 289. The observed multiple forms of TUCANidentified by their different mobilities in SDS-PAGE experiments (seeFIG. 6B, for example) could be differently phosphorylated forms ofTUCAN. TUCAN also contains a candidate caspase cleavage site (DEED) atresidues 243-246.

These and other molecular characteristics and cellular functions ofTUCAN are described, for example, in Pathan et al. J. Biol. Chem.276:32220-32229 (2001), the entirety of which is incorporated herein byreference.

As disclosed herein in Example VII, relatively high levels of TUCAN arefound in several human cancer cell lines. Moreover, as disclosed inExamples II and VIII, compared to normal colonic mucosa, TUCANimmunostaining was pathologically elevated in roughly two-thirds ofearly-stage colon cancers, indicating abnormal over-expression of thisanti-apoptotic protein in association with malignant transformation.Studies of cells derived from pro-caspase-9 knock-out mice haveindicated that pro-caspase-9 functions as a tumor suppressor in ap53-dependent pathway (Soengas et al. Science 284:156-159 (1999)). Inview of the role of TUCAN in regulating pro-caspase-9, over-expressionof TUCAN can be functionally equivalent to loss of pro-caspase-9,indicating that elevated levels of TUCAN can promote tumor pathogenesisor progression. As shown herein in Examples IV and VIII, colon cancerpatients whose tumors contained higher levels of TUCAN indeed were morelikely to die from their disease, based on retrospective analysis usingarchival specimens.

Therefore, the invention provides a method for determining a prognosisfor survival for a cancer patient using TUCAN. The method involves (a)measuring a level of a TUCAN in a neoplastic cell-containing sample fromthe cancer patient, and (b) comparing the level of TUCAN in the sampleto a reference level of TUCAN, wherein low levels of TUCAN in the samplecorrelate with increased survival of the patient.

A level of TUCAN in a neoplastic cell-containing sample that exceeds adetermined basal level, or reference level, of TUCAN can be asignificant factor in tumor recurrence or spread. When tumor celldetermined reference levels are exceeded, the level of TUCAN ischaracterized as high or overproduced. High or overproduced TUCAN can beindicative of increased risk of tumor recurrence or spread. Low orunderproduced TUCAN can be indicative of decreased risk or tumorrecurrence or spread.

The methods of the invention for prognosing survival for a cancerpatient involve obtaining a sample from a patient and measuring thelevel of one or more biomarkers, such as TUCAN. The level of thebiomarker, such as TUCAN, is used to determine the prognosis fordisease-free or overall survival of a cancer patient based on thecorrelations provided herein. Such prognosis is possible because thelikelihood of tumor recurrence or spread correlates with the level ofTUCAN in a tumor cell. For example, as shown in Examples VI and VIII, ithas been found that when the levels of TUCAN expression are low, thelikelihood of cancer recurrence is low. The level of TUCAN in aneoplastic-cell containing sample from a patient can be used as the solefactor in assessing disease status or can be used in addition to otherpredictive methods.

TUCAN can be used to prognose survival or monitor the effectiveness of acourse of treatment for patients suffering from a variety of types ofcancer. As described in Example VII, TUCAN is present in multipledifferent cancer cell types, including leukemia, melanoma and breast,ovarian, lung, CNS, prostate and renal cancers. Also as described above,a cellular function of TUCAN is suppression of mitochondrial signalingpathway-induced apoptosis. Mitochondrial signaling pathway-inducedapoptosis is an apoptotic mechanism that can occur in any cell type, andthat can become dysregulated or suppressed in any type cell, resultingin transformation of a cell such that it proliferates without normalhomeostatic growth control. Therefore, a level of TUCAN can becorrelated with tumor recurrence or survival of a patient having anytype of cancer. Using the guidance provided herein and other well-knownmethods, those skilled in the art will be able to determine if a levelof TUCAN in a particular tumor cell type correlates with patientsurvival. Having determined a correlation between a reference level ofTUCAN and survival of a cancer patient, those skilled in the art canpractice the methods for determining the prognosis for survival for acancer patient and the method for monitoring the effectiveness of acourse of treatment for a patient with cancer described herein.

In the methods of the invention, a sample can be, for example, a cell ortissue obtained using a biopsy procedure or can be a fluid samplecontaining cells, such as blood, serum, semen, urine, or stool. Thoseskilled in the art will be able to determine an appropriate sample,which will depend on cancer type, and an appropriate method forobtaining a biopsy sample, if necessary. When possible, it can bepreferable to obtain a sample from a patient using the least invasivecollection means. For example, obtaining a fluid sample from a patient,such as blood, saliva, serum, semen, urine or stool, is less invasivethan collecting a tissue sample.

In one embodiment, a level of TUCAN can be determined by measuring theamount of a TUCAN using a selective binding agent, such as an antibodyspecifically reactive with a TUCAN polypeptide. Other selective bindingagents include polypeptides that bind to a TUCAN polypeptide, such as aTUCAN polypeptide that contains the TUCAN CARD domain (amino acids345-431 (SEQ ID NO:3)), and a caspase 9 polypeptide, the amino acidsequence of which (SEQ ID NO:4) is referenced as P55211 in the Prositedatabase, or modifications thereof that bind to a TUCAN polypeptide.Selective binding of TUCAN to pro-caspase and to itself is described inExample IX.

Essentially all modes of affinity binding assays are applicable for usein determining a level of TUCAN, or another biomarker polypeptide, suchas cIAP2, Apaf1, Smac, β-catenin, Bcl-2 or p53, in a sample. Suchmethods are rapid, efficient and sensitive. Moreover, affinity bindingmethods are simple and can be modified to be performed under a varietyof clinical settings and conditions to suit a variety of particularneeds. Affinity binding assays that are known and can be used in themethods of the invention include both soluble and solid phase formats. Aspecific example of a soluble phase affinity binding assay isimmunoprecipitation using a biomarker selective antibody or otherbinding agent. Solid phase formats are advantageous for the methods ofthe invention since they are rapid and can be performed more easily onmultiple different samples simultaneously without losing sensitivity oraccuracy. Moreover, solid phase affinity binding assays are furtheramenable to high throughput screening and automation.

Specific examples of solid phase affinity binding assays includeimmunohistochemical binding assays, immunoaffinity binding assays suchas an ELISA and radioimmune assay (RIA). Other solid phase affinitybinding assays are known to those skilled in the art and are applicableto the methods of the invention. Although affinity binding assays aregenerally formatted for use with an antibody binding molecules that isselective for the analyte or ligand of interest, essentially any bindingagent can be alternatively substituted for the selectively bindingantibody. Such binding agents include, for example, macromolecules suchas polypeptides, peptides, nucleic acid molecules, lipids and sugars aswell as small molecule compounds. Methods are known in the art foridentifying such molecules which bind selectively to a particularanalyte or ligand and include, for example, surface display librariesand combinatorial libraries. Thus, for a molecule other than an antibodyto be used in an affinity binding assay, all that is necessary is forthe binding agent to exhibit selective binding activity for a biomarker.

The various modes of affinity binding assays, such as immunoaffinitybinding assays, include, for example, immunohistochemistry methods,solid phase ELISA and RIA as well as modifications thereof. Suchmodifications thereof include, for example, capture assays and sandwichassays as well as the use of either mode in combination with acompetition assay format. The choice of which mode or format ofimmunoaffinity binding assay to use will depend on the intent of theuser. Such methods can be found described in common laboratory manualssuch as Harlow and Lane, Using Antibodies: A Laboratory Manual, ColdSpring Harbor Laboratory Press, New York (1999).

An antibody useful in the methods of the invention includes a polyclonaland monoclonal antibody, as well as an antigen binding fragment of suchantibodies. Methods of preparing polyclonal or monoclonal antibodies arewell known to those skilled in the art and are described in Example Iand in Harlow and Lane, Antibodies: A Laboratory Manual, Cold SpringHarbor Laboratory Press (1988).

An antibody useful in the methods of the invention also includesnaturally occurring antibodies as well as non-naturally occurringantibodies, including, for example, single chain antibodies, chimeric,bifunctional and humanized antibodies, as well as antigen-bindingfragments thereof. Such non-naturally occurring antibodies can beconstructed using solid phase peptide synthesis, can be producedrecombinantly or can be obtained, for example, by screeningcombinatorial libraries consisting of variable heavy chains and variablelight chains as described by Huse et al. (Science 246:1275-1281 (1989)).These and other methods of making, for example, chimeric, humanized,CDR-grafted, single chain, and bifunctional antibodies are well known tothose skilled in the art (Winter and Harris, Immunol. Today 14:243-246(1993); Ward et al., Nature 341:544-546 (1989); Harlow and Lane, supra,1988); Hilyard et al., Protein Engineering: A practical approach (IRLPress 1992); Borrabeck, Antibody Engineering, 2d ed. (Oxford UniversityPress 1995)).

Formats employing affinity binding can be used in conjunction with avariety of detection labels and systems known in the art to quantitateamounts of biomarkers in the analyzed sample. Detection systems includethe detection of bound biomarker by both direct and indirect means.Direct detection methods include labeling of the biomarker-specificallyreactive antibody or binding agent. Indirect detection systems include,for example, the use of labeled secondary antibodies and binding agents.

Secondary antibodies, labels and detection systems are well known in theart and can be obtained commercially or by techniques well known in theart. The detectable labels and systems employed with thebiomarker-selective binding agent should not impair binding of the agentto the biomarker. Moreover, multiple antibody and label systems can beemployed for detecting the bound biomarker-specifically reactiveantibody to enhance the sensitivity of the binding assay if desired.

Detectable labels can be essentially any label that can be quantitatedor measured by analytical methods. Such labels include, for example,enzymes, radioisotopes, fluorochromes as well as chemi- andbioluminescent compounds. Specific examples of enzyme labels includehorseradish peroxidase (HRP), alkaline phosphatase (AP),β-galactosidase, urease and luciferase.

A horseradish-peroxidase detection system can be used, for example, withthe chromogenic substrate tetramethylbenzidine (TMB), which yields asoluble product in the presence of hydrogen peroxide that is detectableby measuring absorbance at 450 nm. An alkaline phosphatase detectionsystem can be used with the chromogenic substrate p-nitrophenylphosphate, for example, which yields a soluble product readilydetectable by measuring absorbance at 405 nm. Similarly, aβ-galactosidase detection system can be used with the chromogenicsubstrate o-nitrophenyl-β-D-galactopyranoside (ONPG), which yields asoluble product detectable by measuring absorbance at 410 nm, or aurease detection system can be used with a substrate such asurea-bromocresol purple (Sigma Immunochemicals, St. Louis, Mo.).Luciferin is the substrate compound for luciferase which emits lightfollowing ATP-dependent oxidation.

Fluorochrome detection labels are rendered detectable through theemission of light of ultraviolet or visible wavelength after excitationby light or another energy source. DAPI, fluorescein, Hoechst 33258,R-phycocyanin, B-phycoerythrin, R-phycoerythrin, rhodamine, Texas redand lissamine are specific examples of fluorochrome detection labelsthat can be utilized in the affinity binding formats of the invention. Aparticularly useful fluorochrome is fluorescein or rhodamine.

Chemiluminescent as well as bioluminescent detection labels areconvenient for sensitive, non-radioactive detection of a biomarker andcan be obtained commercially from various sources such as AmershamLifesciences, Inc. (Arlington Heights, Ill.).

Alternatively, radioisotopes can be used as detectable labels in themethods of the invention. Iodine-125 is a specific example of aradioisotope useful as a detectable label.

Signals from detectable labels can be analyzed, for example, using aspectrophotometer to detect color from a chromogenic substrate; afluorometer to detect fluorescence in the presence of light of a certainwavelength; or a radiation counter to detect radiation, such as a gammacounter for detection of iodine-125. For detection of an enzyme-linkedsecondary antibody, for example, a quantitative analysis of the amountof bound agent can be made using a spectrophotometer such as an EMAXMicroplate Reader (Molecular Devices, Menlo Park, Calif.) in accordancewith the manufacturer's instructions. If desired, the assays of theinvention can be automated or performed robotically, and the signal frommultiple samples can be detected simultaneously.

The prognostic formats of the present invention can be forward, reverseor simultaneous as described in U.S. Pat. No. 4,376,110 and No.4,778,751. Separation steps for the various assay formats describedherein, including the removal of unbound secondary antibody, can beperformed by methods known in the art (Harlow and Lane, supra). Forexample, washing with a suitable buffer can be followed by filtration,aspiration, vacuum or magnetic separation as well as by centrifugation.

A binding agent selective for a biomarker also can be utilized inimaging methods that are targeted at biomarker—expressing neoplasticcells. These imaging techniques will have utility in identification ofresidual neoplastic cells at the primary site following standardtreatments including, for example, surgical resection of an organ of thegastrointestinal system, such as the colon, and radiation therapy. Inaddition, imaging techniques that detect neoplastic cells have utilityin detecting secondary sites of metastasis. The biomarker specificbinding agent can be radiolabeled with, for example, ¹¹¹indium andinfused intravenously as described by Kahn et al., Journal of Urology152:1952-1955 (1994). The binding agent selective for a biomarker canbe, for example, a monoclonal antibody specifically reactive with TUCANor another biomarker, such as cIAP2, Apaf1, Smac, β-catenin, Bcl-2 orp53. Imaging can be accomplished by, for example,radioimmunoscintigraphy as described by Kahn et al., supra.

The level of TUCAN, or another biomarker, such as cIAP2, Apaf1, Smac,β-catenin, Bcl-2 or p53, also can be determined by measuring the amountof a biomarker mRNA or DNA using a binding agent selective for thebiomarker, such as a nucleic acid probe. The methods used to detect mRNAlevels include detection of hybridization or amplification of mRNAencoding the biomarker. This detection can be carried out by analysis ofmRNA either in vitro or in situ using one of the methods known to one ofordinary skill in the art as exemplified in the Current Protocols inMolecular Biology (John Wiley & Sons, 1999); in U.S. Pat. No. 5,882,864;and the like. A TUCAN mRNA, or other biomarker mRNA, detected will beany RNA transcript of a TUCAN gene, or fragment thereof, or cIAP2,Bcl-2, p53, β-catenin, survivin or Apaf1 gene, or fragment thereof.

There are numerous methods well known in the art for detecting nucleicacid molecules by specific or selective hybridization with acomplementary probe. Briefly, for detection by hybridization, a TUCANnucleic acid probe complementary to a TUCAN gene, having a detectablelabel is added to a neoplastic cell-containing sample obtained from theindividual having, or suspected of having cancer under conditions whichallow annealing of the probe to TUCAN RNA. Methods for detecting TUCANRNA in a sample can include the use of, for example, RT-PCR. Conditionsare well known in the art for both solution and solid phasehybridization procedures. Moreover, optimization of hybridizationconditions can be performed, if desired, by hybridization of an aliquotof the sample at different temperatures, durations and in differentbuffer conditions. Such procedures are routine and well known to thoseskilled. Following annealing, the sample is washed and the signal ismeasured and compared with a suitable control or standard value. Themagnitude of the hybridization signal is directly proportional to themRNA level of TUCAN. A level of TUCAN mRNA in a neoplasticcell-containing sample is compared to a suitable reference level forTUCAN mRNA. The levels of other biomarker mRNA, such as cIAP2, Apaf1,Smac, β-catenin, Bcl-2 or p53, can be similarly determined and comparedto a suitable reference level for the particular biomarker.

Other examples of methods include PCR and other amplification methodssuch as RT-PCR, 5′ or 3′ RACE, RNase protection, RNA blot, dot blot orother membrane-based technologies, dip stick, pin, ELISA ortwo-dimensional arrays immobilized onto a solid support. These methodscan be performed using either qualitative or quantitative measurements,all of which are well known to those skilled in the art.

PCR or RT-PCR can be used with isolated RNA or crude cell lysatepreparations. PCR is advantageous when there is limiting amounts ofstarting material. A further description of PCR methods can be found in,for example, Dieffenbach, C. W., and Dveksler, G. S., PCR Primer: ALaboratory Manual, Cold Spring Harbor Press, Plainview, N.Y. (1995).Multisample formats such as microarrays offer the advantage of analyzingnumerous, different samples in a single assay. In contrast, solid-phasedip stick-based methods offer the advantage of being able to rapidlyanalyze a patient's fluid sample for an immediate result.

Nucleic acid probes useful for measuring the expression level of abiomarker, such as cIAP2, TUCAN, Apaf1, β-catenin, Bcl-2, or Smac byhybridization include, for example, probes prepared using the nucleotidesequences provided herein. Nucleic acid molecules corresponding to theentire cDNA sequences and fragments thereof, including oligonucleotidescorresponding to cIAP2, TUCAN, Apaf1, β-catenin, Bcl-2, or Smacnucleotide sequences and which are capable of specifically orselectively hybridizing to cIAP2, TUCAN, Apaf1, β-catenin, Bcl-2, orSmac RNA, are useful for hybridization methods.

A reference level is a level a biomarker, such as cIAP2, TUCAN, Apaf1,Smac, β-catenin, of Bcl-2, used to evaluate the level of the biomarkerin cancerous cells of a patient. Specifically, when the level of abiomarker in the cancerous cells of a patient are higher than thereference level, the cells will be considered to have a high level of,or overproduction, of the biomarker. Conversely, when the level ofbiomarker in the cancerous cells of a patient are lower than thereference level, the cells will be considered to have a low level of, orunderproduction, of the biomarker.

A high level of a biomarker, such as cIAP2, TUCAN, Apaf1, Smac,β-catenin, Bcl-2 or p53, or overproduction of a biomarker gene isrelated to a level of the biomarker above a determined basal level.Thus, a reference or basal level of a biomarker, such as cIAP2, TUCAN,Apaf1, Smac, β-catenin, Bcl-2 or p53, in a cancer cell is identified asa “cutoff” value, above which there is a significant correlation betweenthe presence of the biomarker and increased or decreased tumorrecurrence or spread. Those of skill in the art will recognize that some“cutoff” values are not sharp in that clinical correlations are stillsignificant over a range of values on either side of the cutoff;however, it is possible to select an optimal cutoff value (for examplevarying H-scores, and the like) of a level of a biomarker for a cancercell type. It is understood that improvements in optimal cutoff valuescould be determined, depending on the sophistication of statisticalmethods used and on the number and source of samples used to determinereference or basal values.

Such overproduction is not typically calculated in terms of absolutebiomarker levels, but is determined using relative measurements. Theserelative measurements are illustrated for quantitation purposes with aninternal standard; however, it will be appreciated that other standardsor methods of determination can be used, such as comparison withexternal standards, biomarker polypeptide measurements, biomarker mRNAmeasurements, absolute values of protein, mRNA or DNA levels, and thelike.

A reference level can also be determined by comparison of biomarkerlevels in populations of patients having cancer, such as patients havingcancer of the same stage. This can be accomplished by histogramanalysis, in which the entire cohort of patients tested are graphicallypresented, wherein a first axis represents the level of a biomarker, anda second axis represents the number of patients in the cohort whosetumor cells contain the biomarker at a given level. Two or more separategroups of patients can be determined by identification of subsetspopulations of the cohort which have the same or similar levels of thebiomarker. Determination of the reference level can then be made basedon a biomarker level that best distinguishes these separate groups.

Verification that the reference level distinguishes the likelihood oftumor recurrence or spread in cancer patients expressing below-referencebiomarker levels versus cancer patients expressing above-referencebiomarker levels can be carried out using single variable ormulti-variable analysis. These methods determine the likelihood of acorrelation between one or more variables and a given outcome. In thespecific case, the methods will determine the likelihood of acorrelation between a biomarker levels (or biomarker level coupled withanother variable) and disease-free or overall survival of cancerpatients. Any one of a plurality of methods well known to those ofordinary skill in the art for carrying out these analyses can be used.Examples of single variable analysis is the Kaplan-Meir method or thelog-rank test. An example of multi-variable analysis is the Coxproportional-hazards regression model (see, for example, Example VI).

Population-based determination of reference levels, for example, byhistogram analysis can be carried out using a cohort of patientssufficient in size in order to determine two or more separate groups ofpatients having different biomarker levels. Typically, such a cohortcomprises at least 25 patients, such as at least 50 patients, includingat least 75 patients, and at least 100 patients. Similarly, verificationof determined reference levels can also comprise at least 25 patients,such as at least 50 patients, including at least 75 patients, and atleast 100 patients.

The reference level can be a single number, equally applicable to everypatient, or the reference level can vary according to specificsubpopulations of patients. For example, men might have a differentreference level than women for the same cancer. Furthermore, thereference level can be a level determined for each patient individually.For example, the reference level might be a certain ratio of a biomarkerlevel in the tumor cells of a patient relative to the biomarker level innon-tumor cells within the same patient. Thus the reference level foreach patient can be proscribed by a reference ratio of biomarker levels,wherein the reference ratio can be determined by any of the methods fordetermining the reference levels described above.

Further, while a reference level can separate two groups of patients, itis within the scope of the invention that numerous reference valuesmight exist which separate a plurality of populations. For example, tworeference values can separate a first group of patients with high levelsof a biomarker from a second group of patients with intermediate levelsthe biomarker, and from a third group of patients with low levels of thebiomarker. The number of different reference levels can be sufficient toproscribe a curve, such as a continuous line, which describes thelikelihood of disease-free or overall survival in a patient as afunction of the biomarker level in that patient. Such a curve willconstitute a “continuous” biomarker level, where the likelihood ofdisease free or overall survival in a patient is proportional to thebiomarker level in that patient. Two or more biomarker levels also canbe represented by such a curve.

The reference level can also represent the level of a biomarker protein,such as cIAP2, TUCAN, Apaf1, Smac, β-catenin, Bcl-2 or p53, in one ormore compartments of the cell. Typically, the reference level willrepresent the level of biomarker protein in (a) the whole cell, (b) thenucleus, or (c) the cytosol. This level will be useful when cellcompartmentalization of the protein correlates with the risk of tumorrecurrence or spread of a certain cancer. Similarly, the reference levelcan be a ratio of levels of biomarker protein in the differentcompartments (for example, the ratio of nuclear biomarker protein towhole cell biomarker protein, or the ratio of nuclear to cytosolicbiomarker protein).

The reference level of a biomarker, such as cIAP2, TUCAN, Apaf1, orSmac, can further be used in conjunction with another variable found tobe a statistically significant indicator of the likelihood ofdisease-free or overall survival for cancer. Such indicators include thepresence or levels of known cancer markers (for example, colon cancermarkers include sialosyl-TnCEA, CA19-9, and LASA), or can be clinical orpathological indicators (for example, age, tumor size, tumor histology,clinical stage, family history and the like). For example, clinicalstage of the cancer is also a statistically significant indicator ofdisease-free or overall survival, wherein the reference level of abiomarker can vary according to the clinical stage of the cancer. Forexample, the level of a biomarker, such as a low level of TUCAN, inconjunction with clinical stage II of a cancer for a given patient,together are indicators for increased likelihood of disease free oroverall survival. Hence, the reference level of a biomarker can vary asa function of another statistically significant indicator ofdisease-free or overall survival for cancer.

The levels of biomarkers, such as cIAP2, Apaf1, TUCAN, Bcl-2 and Smac,in a cancer cell can correlate with each other and with other moleculesbecause these molecules participate in common dysregulated molecularpathways that contribute to the hyperproliferative state of a cancercell. Therefore a combination of TUCAN with one or more additionalbiomarkers can be used in the methods of the invention for determining aprognosis for survival for a cancer patient. A second or additionalbiomarker can be, for example, Apaf1, cIAP1, cIAP2, survivin, AIF,Bcl-2, Bcl-XL, Bax, Bid, BAG1, p53, mutant p53, β-catenin, MIB-1 oranother well-known tumor marker, such as the exemplary commerciallyavailable tumor markers described below. Furthermore, the use of acombination of TUCAN with one or more biomarkers can provide increasedprognostic significance or confidence in a prognostic determination.

Therefore, the invention provides a method for determining a prognosisfor survival for a cancer patient that involves the use of two or morebiomarkers. The method is practiced by (a) measuring the levels of TUCANand one or more biomarkers selected from the group consisting of cIAP2,Apaf1, Bcl-2 and Smac in a neoplastic cell-containing sample from thecancer patient, and (b) comparing the level of TUCAN and the one or moreselected biomarkers in the sample to a reference level of TUCAN and thebiomarkers, wherein a low level of TUCAN and a high level of any ofApaf1, Bcl-2 or Smac, or a low level of TUCAN and a low level of cIAP2,in said sample correlate with increased survival of said patient.

The methods of the invention can be practiced, for example, by selectinga combination of TUCAN and one or more biomarkers for which increased ordecreased expression correlates with improved survival, such as any ofcIAP2, Apaf1, Bcl-2, Smac, or another known or standard biomarker forcancer. The selected biomarker can be a general diagnostic or prognosticmarker useful for multiple types of cancer, such as CA 125, CEA or LDH,or can be a cancer-specific diagnostic or prognostic marker, such as acolon cancer marker (for example, sialosyl-TnCEA, CA19-9, or LASA),breast cancer marker (for example, CA 15-2. Her-2/neu and CA 27.29),ovarian cancer marker (for example, CA72-4), lung cancer (for example,neuron-specific enolase (NSE) and tissue polypeptide antigen (TPA)),prostate cancer (for example, PSA, prostate-specific membrane antigenand prostatic acid phosphatase), melanoma (for example, S-100 andTA-90), as well as other biomarkers specific for other types of cancer.Those skilled in the art will be able to select useful diagnostic orprognostic markers for detection in combination with TUCAN. Similarly,three or more, four or more or five or more or a multitude of biomarkerscan be used together for determining a prognosis for survival for acancer patient.

The use of two or more biomarkers can provide increased confidence inprognostic outcome. For example, as disclosed herein, combinations oflow cIAP and low TUCAN, and high Apaf1 and low TUCAN were correlatedwith increased disease-free survival (see Example V). In particular,among 33 patients examined for levels of cIAP and TUCAN in a neoplasticcell-containing sample, 97% of patients having low cIAP and low TUCANremained alive (91% disease-fee), as opposed to 56% alive and 44%disease-free for other categories of patients. In addition, among 17patients examined for levels of Apaf1 and TUCAN in a neoplasticcell-containing sample, 100% of patients having high Apaf1 and low TUCANremained alive and disease-free, as opposed to 65% alive and 53%disease-free for other categories of patients. Those skilled in the artwill recognize that such correlations can be observed using othercombinations of biomarkers using methods described herein.

Combinations of biomarkers useful in the prognostic methods of theinvention include, for example, cIAP2 and TUCAN, Apaf1 and TUCAN, and amultiplicity of other combination of TUCAN with biomarkers such ascIAP2, Apaf1, Bcl-2 and Smac and other molecules, including AIF, Bcl-2,Bcl-XL, Bax, Bid, BAG1, p53, mutant p53, β-catenin, MIB-1 and a varietyof other general and tumor-specific biomarkers, such as commerciallyavailable diagnostic markers described herein above. Such combinationscan be useful indicators of the metastatic state of a cancer cellbecause elevated levels of these biomarkers was observed in a portion ofall cancer specimens evaluated (see Example II). Further, elevatedlevels of various biomarkers correlated with another colon cancermarker, Ki-67, and positive correlations between the expression ofbiomarkers was observed, for example, between cIAP2 and TUCAN (p=0.003)in patient populations.

The invention also provides a method for monitoring the effectiveness ofa course of treatment for a patient with cancer. The method involves (a)determining the level of a TUCAN in a neoplastic cell-containing samplefrom the cancer patient, and (b) determining the level of TUCAN in aneoplastic cell-containing sample from the patient after treatment,whereby comparison of the TUCAN level prior to treatment with thebiomarker level after treatment indicates the effectiveness of thetreatment.

As used in the context of a course of treatment, “effectiveness” refersto the ability of the course of treatment to decrease the risk of tumorrecurrence or spread and therefore to increase the likelihood ofdisease-free or overall survival of the patient. This method will haveparticular utility when the level a biomarker, such as cIAP, TUCAN,Apaf1 or Smac, in the tumor cells of a patient is abnormal compared tothe level of cIAP, TUCAN, Apaf1 and Smac in the non-tumor cells of thepatient. Comparison of biomarker levels in a neoplastic cell-containingsample from a patient before and after treatment will thereby serve toindicate whether a biomarker level is returning to that of non-tumorcells, implying a more effective course of treatment, or whether abiomarker level is remaining abnormal or increasing in abnormality,implying a less effective course of treatment. For example, an increasein the level of Apaf1, Bcl-2 or Smac in a patient sample after treatmentindicates that treatment is effective because high levels of Apaf1 orSmac correlate with a lower incidence of colon cancer recurrence.Further, a low in the level of β-catenin, cIAP2 or TUCAN in a patientsample after treatment indicates that treatment is effective because lowlevels of β-catenin, cIAP2 or TUCAN correlate with a lower incidence ofcolon cancer recurrence.

Patients having cancer can be classified according to whether a highlevel of a particular biomarker, or a low level of the biomarker, ismeasured in a neoplastic cell-containing sample obtained from thepatient. Determination of the prognosis for the patient can be made bydetermining whether the group to which the patient has been assignedcorrelates with a higher or lower likelihood of disease-free or overallsurvival with respect to the group to which the patient was notassigned.

Therefore, the invention also provides a method of determining aprognosis for survival for a cancer patient that involves patientclassification. The method is practiced by (a) measuring a level ofTUCAN in a neoplastic cell-containing sample from the cancer patient,and (b) classifying the patient as belonging to either a first or secondgroup of patients, wherein the first group of patients having low levelsof TUCAN is classified as having an increased likelihood of survivalcompared to the second group of patients having high levels of TUCAN.

A high level of TUCAN, or overproduction of TUCAN, correlates withpatients having an increased risk of tumor recurrence or spread. Thus,patients belonging to a first group having high levels of TUCAN areclassified as having an increased risk of tumor recurrence or spreadcompared to a second group of patients having low levels TUCAN. Patientsbelonging to a first group having low levels of TUCAN are classified ashaving increased likelihood of survival compared to a second group ofpatients having high levels of TUCAN.

The method of determining a prognosis for survival for a cancer patientcan be practiced using one or more additional biomarkers. A variety ofbiomarkers, including known cancer markers and the prognostic biomarkersdisclosed herein, can be used in combination with TUCAN to determine aprognosis for survival for a cancer patient. In one embodiment, themethod involves (a) determining a level of cIAP2 the neoplasticcell-containing sample from the cancer patient, and (b) classifying thepatient as belonging to either a first or second group of patient,wherein the first group of patients having low levels of TUCAN and lowlevels of cIAP2 is classified as having increased likelihood of survivalcompared to the second group of patients having high levels of TUCAN andhigh levels of cIAP2.

In another embodiment, the method involves (a) determining a level of abiomarker selected from the group consisting of Apaf1, Smac and Bcl-2 inthe neoplastic cell-containing sample from the cancer patient, and (b)classifying the patient as belonging to either a first or second groupof patient, wherein the first group of patients having low levels ofTUCAN and high levels of any of Apaf1, Smac or Bcl-2 is classified ashaving increased likelihood of survival compared to the second group ofpatients having high levels of TUCAN and low levels of any of Apaf1,Smac or Bcl-2.

After the levels of one or more biomarker in patient sample have beendetermined and compared to a reference level, the patient is thenclassified into a group having a certain likelihood of disease free oroverall survival. Then the likelihood of disease-free or overallsurvival for the patient is assessed based on the likelihood ofdisease-free or overall survival for patients in that group.

For example, a neoplastic cell containing sample from a cancer patientcan be determined to have high levels of Apaf1, Bcl-2 or Smac relativeto a reference level. This patient would then be classified into a groupof patients having high levels of Apaf1, Bcl-2 or Smac. Because it hasbeen discovered that there is an increased likelihood of disease-free oroverall survival for the group of patients expressing high levels ofApaf1, Bcl-2 or Smac in cancer cells (relative to those expressing lowlevels of Apaf1, Bcl-2 or Smac in cancer cells), the specific cancerpatient would be considered to have an increased likelihood of diseasefree or overall survival.

Conversely, a neoplastic cell containing sample from a cancer patientcan be determined to have high levels of cIAP2, β-catenin or TUCANrelative to a reference level. This patient would then be classifiedinto a group of patients having high levels of cIAP2, β-catenin orTUCAN. Because it has been discovered that there is a decreasedlikelihood of disease-free or overall survival for the group of patientsexpressing high levels of cIAP2, β-catenin or TUCAN in cancer cells(relative to those expressing low levels of cIAP2, β-catenin or TUCAN incancer cells), the specific cancer patient would be considered to havean decreased likelihood of disease free or overall survival.

The methods of the invention are applicable to determining thesusceptibility of an individual for developing cancer. The methods areapplicable to a variety of cancers, including gastrointestinal, lung,colon, prostate, breast, ovarian, skin, blood and kidney cancers. Inparticular, colon cancers develop from premalignant precursor lesionsknown as adenomatous colon polyps. Multiple epidemiological studies havedemonstrated that once one member of a family has developed anadenomatous colon polyp, his or her siblings are at markedly elevatedrisk for developing both colon adenomas and colon cancers. Those skilledin the art understand that the method of the invention can be practicedas described herein for neoplastic conditions, including colonneoplastic conditions, such as adenomatous colon polyps, for example, bycollecting an appropriate biopsy sample.

The methods of the invention for determining a prognosis for survivalfor a cancer patient are applicable to patients at any stage of tumorprogression, and further can be used to determine a stage of tumorprogress. A stage of a tumor refers to the degree of progression of atumor. Various stages of tumor development are well known to those ofskill in the art, as exemplified in Markman, “Basic Cancer Medicine,”Saunders, (ed. Zorab, R.) (1997). For example, cancers can be stagedinto three general stages—localized, regional spread, and distantspread. Cancers also can be staged using the TNM system, which considersthe extent of direct spread within affected and nearby tissues, theextent of spread to nearby lymph nodes, and the extent of spread todistant organs. Based on these features, spread of cancers can besummarized by assigning Roman numerals from 0 through IV. Those skilledin the art can select an appropriate staging system for a particulartype of cancer.

In particular, colon cancer can be staged using the Dukes, Astler-Collerand AJCC/TNM systems, which describe the spread of the cancer inrelation to the layers of the wall of the colon or rectum, organs nextto the colon and rectum, and other organs farther away. Dukes stage A isequivalent to AJCC/TNM stage I and Astler-Coller stage A, B1; Duke'sstage B is equivalent to AJCC/TNM stage II and Astler-Coller stage B2,B3. Dukes stage C is equivalent to AJCC/TNM stage III and Astler-Collerstage C1, C2, C3. AJCC/TNM stages of colorectal cancer are as follow:Stage 0: the cancer has not grown beyond the inner layer (mucosa) of thecolon or rectum. This stage is also known as carcinoma in situ orintramucosal carcinoma; Stage I: the cancer has grown through the mucosainto the submucosa, or can also have grown into the muscularis propria,but it has not spread outside the wall itself into nearby tissue such aslymph nodes; Stage II: the cancer has grown through the wall of thecolon or rectum, into the outermost layers and may have invaded othernearby tissues, but has not yet spread to the nearby lymph nodes; StageIII: the cancer can be of any size, but has spread to 3 or fewer nearbylymph nodes, or has spread to 4 or more nodes but it has not spread toother parts of the body; Stage IV: the cancer has spread to distantorgans such as the liver, lung, peritoneum or ovary.

Early stages of tumor development shall be understood to refer to stagesin tumor development in which the tumor has detectably spread no furtherthan the lymph nodes local to the organ of the primary tumor. Typically,early stages will be considered to be stages I and II.

The predictive value of the method of the invention will be particularlyeffective in the case of patients in the early stages of cancer. This isbecause the method of the invention is advantageously effective indetermining the risk of metastasis in patients who demonstrate nomeasurable metastasis at the time of examination. One of ordinary skillin the art would appreciate that the prognostic indicators of survivalfor cancer patients suffering from stage I cancer may be different fromthose for cancer patients suffering from stage IV cancer. For example,prognosis for stage I cancer patients may be oriented toward thelikelihood of continued growth and/or metastasis of the cancer, whereasprognosis for stage IV cancer patients may be oriented toward the likelyeffectiveness of therapeutic methods for treating the cancer.

A stage of cancer progression can be correlated with a level of one ormore biomarkers, such as a level of TUCAN, Apaf1 or an IAP, such ascIAP2 or Smac. Therefore, a determination of a level of a biomarker in asample from a cancer patient can be used to determine a stage of thetumor from which the sample was derived by comparing the sample with areference level of the biomarker indicative of a particular stage ofcancer.

The methods of the invention are applicable for use with a variety ofdifferent types of samples isolated or obtained from an individualhaving, or suspected of having a cancer or neoplastic condition. Forexample, samples applicable for use in one or more prognostic formats ofthe invention, include tissue and cell samples. A tissue or cell samplecan be obtained, for example, from a fluid sample obtained from thepatient, by biopsy or surgery. For example, in the case of solid tumorswhich have not metastasized, a tissue sample from the surgically removedtumor can be obtained and prepared for testing by conventionaltechniques. In addition, a sample can be removed from a patient, forexample, using well-known biopsy procedures. For example, in the case ofcolon cancer, to obtain a sample of very small, raised polyps, acolonoscope can be fitted with a snare to remove a polyp without damageto the wall of the colon (polypectomy); or to obtain small, flatterpolyps, a biopsy forceps can be attached to a colonoscope to collect asmall sample of tissue.

As described below, and depending on the format of the method, thetissue can be used whole or subjected to various methods known in theart to disassociate the sample into smaller pieces, cell aggregates orindividual cells. Additionally, when combined with amplification methodssuch as polymerase chain reaction (PCR), a single cell sample issufficient for use in prognostic assays of the invention which employhybridization detection methods. Similarly, when measuring biomarkerpolypeptide levels, amplification of the signal with enzymatic couplingor photometric enhancement can be employed using only a few or a smallnumber of cells.

Whole tissue obtained from a biopsy or surgery is one example of aneoplastic cell-containing sample. Tumor tissue cell samples can beassayed employing any of the formats described below. For example, thetumor tissue sample can be mounted and hybridized in situ with biomarkernucleic acid probes. Similar histological formats employing proteindetection methods and in situ activity assays also can be used to detecta biomarker polypeptide in whole tissue tumor cell samples. Proteindetection methods include, for example, staining with a biomarkerspecific antibody, as described herein, in Example II. Such histologicalmethods as well as others well known to those skilled in the art areapplicable for use in the prognostic methods of the invention usingwhole tissue as the source of a neoplastic cell-containing sample.Methods for preparing and mounting the samples are similarly well knownin the art.

Individual cells and cell aggregates from an individual having, orsuspected of having a neoplastic condition or cancer is another exampleof a neoplastic cell-containing sample that can be analyzed forincreased or decreased expression of biomarker RNA or polypeptide. Thecells can be grown in culture and analyzed using procedures such asthose described above. Whole cell samples expressing cell surfacemarkers associated with biomarker expression can be rapidly tested usingfluorescent or magnetic activated cell sorting (FACS or MACS) withlabeled binding agents selective for the surface marker or using bindingagents selective for specific cell populations, for example, and thendetermining a level of a biomarker within this population. A level of abiomarker can be determined using, for example, binding specificallyreacting agents for a biomarker or by hybridization to a biomarkerspecific probe. Other methods for measuring the level of a biomarker inwhole cell samples are known in the art and are similarly applicable inany of the prognostic formats described below.

The tissue or whole cell tumor cell sample obtained from an individualalso can be analyzed for increased or decreased biomarker levels bylysing the cell and measuring the level of a biomarker in the lysate, afractionated portion thereof or a purified component thereof using anyof formats described herein. For example, if a hybridization format isused, biomarker RNA can be amplified directly from the lysate using PCR,or other amplification procedures well known in the art such as RT-PCR,5′ or 3′ RACE to directly measure the level of a biomarker nucleic acidmolecules. RNA also can be isolated and probed directly such as bysolution hybridization or indirectly by hybridization to immobilizedRNA. Similarly, when determining a level of a biomarker usingpolypeptide detection formats, lysates can be assayed directly, or theycan be further fractionated to enrich for a biomarker. For example, animmunochemical method, such as immunoblot analysis (see Example III) canbe performed using a neoplastic cell-containing sample. Numerous othermethods applicable for use with whole tumor cell samples are well knownto those skilled in the art and can accordingly be used in the methodsof the invention.

The tumor tissue or cell sample can be obtained directly from theindividual or, alternatively, it can be obtained from other sources fortesting. Similarly, a cell sample can be tested when it is freshlyisolated or it can be tested following short or prolonged periods ofcryopreservation without substantial loss in accuracy or sensitivity. Ifthe sample is to be tested following an indeterminate period of time, itcan be obtained and then cryopreserved, or stored at 4^(o) C for shortperiods of time, for example. An advantage of the prognostic methods ofthe invention is that they do not require histological analysis of thesample. As such, the sample can be initially disaggregated, lysed,fractionated or purified and the active component stored for laterdiagnosis.

The prognostic methods of the invention are applicable for use with avariety of different types of samples other than tumor cell samples. Forexample, a biomarker polypeptide or fragment thereof that is releasedinto the extracellular space, including circulatory fluids as well asother bodily fluids, can be used in prognostic methods to detect asecreted polypeptide or fragment related to a biomarker polypeptide. Insuch a case, the methods of the invention are applicable with fluidsamples collected from an individual having, or suspected of having aneoplastic condition or cancer.

Fluid samples, which can be measured for biomarker levels, include, forexample, blood, serum, lymph, urine and stool. Other bodily fluids areknown to those skilled in the art and are similarly applicable for useas a sample in the prognostic methods of the invention. One advantage ofanalyzing fluid samples is that they are readily obtainable, insufficient quantity, without invasive procedures as required by biopsyand surgery. Analysis of fluid samples such as blood, serum and urinewill generally be in the prognostic formats described herein whichmeasure biomarker polypeptide levels. As the biomarker relatedpolypeptide is circulating in a soluble form, the methods will besimilar to those which measure expression levels from cell lysates,fractionated portions thereof or purified components.

Neoplastic conditions and cancer can be diagnosed, predicted orprognosed by measuring a level of a biomarker in a neoplasticcell-containing sample, circulating fluid or other bodily fluid obtainedfrom the individual. As described herein, levels of a biomarker can bemeasured by a variety methods known in the art.

One skilled in the art can readily determine an appropriate assay systemgiven the teachings and guidance provided herein and choose a methodbased on measuring RNA or polypeptide. Considerations such as the sampletype, availability and amount will also influence selection of aparticular prognostic format. For example, if the sample is a tumor cellsample and there is only a small amount available, then prognosticformats which measure the amount of biomarker RNA by, for example, PCRamplification, or which measure biomarker polypeptide by, for example,FACS analysis can be appropriate choices for determining the level of abiomarker. Alternatively, if the sample is a blood sample and the useris analyzing numerous different samples simultaneous, such as in aclinical setting, then a multisample format, such as an Enzyme LinkedImmunoabsorbant Assay (ELISA), which measures the amount of a biomarkerpolypeptide can be an appropriate choice for determining the level of abiomarker. Additionally, biomarker nucleic acid molecules released intobodily fluids from the neoplastic or pathological cells can also beanalyzed by, for example, PCR or RT-PCR. Those skilled in the art willknow, or can determine which format is amenable for a particularapplication and which methods or modifications known within the art arecompatible with a particular type of format.

Nucleic acid probes can be produced recombinantly or chemicallysynthesized using methods well known in the art. Additionally,hybridization probes can be labeled with a variety of detectable labelsincluding, for example, radioisotopes, fluorescent tags, reporterenzymes, biotin and other ligands. Such detectable labels canadditionally be coupled with, for example, calorimetric or photometricindicator substrate for spectrophotometric detection. Methods forlabeling and detecting such probes are well known in the art and can befound described in, for example, Sambrook et al., Molecular Cloning: ALaboratory Manual, 2nd ed., Cold Spring Harbor Press, Plainview, N.Y.(1989), and Ausubel et al., Current Protocols in Molecular Biology(Supplement 47), John Wiley & Sons, New York (1999).

Nucleic acid probes useful for detecting a biomarker in a sample can behybridized under various stringency conditions readily determined by oneskilled in the art. Depending on the particular assay, one skilled inthe art can readily vary the stringency conditions to optimize detectionof a particular biomarker in a particular sample type.

In general, the stability of a hybrid is a function of the ionconcentration and temperature. Typically, a hybridization reaction isperformed under conditions of lower stringency, followed by washes ofvarying, but higher, stringency. Moderately stringent hybridizationrefers to conditions that permit a nucleic acid molecule such as a probeto bind a complementary nucleic acid molecule. The hybridized nucleicacid molecules generally have at least 60% identity, at least 75%identity, at least 85% identity; or at least 90% identity. Moderatelystringent conditions are conditions equivalent to hybridization in 50%formamide, 5× Denhart's solution, 5×SSPE, 0.2% SDS at 42° C., followedby washing in 0.2×SSPE, 0.2% SDS, at 42° C. High stringency conditionscan be provided, for example, by hybridization in 50% formamide, 5×Denhart's solution, 5×SSPE, 0.2% SDS at 42° C., followed by washing in0.1×SSPE, and 0.1% SDS at 65° C.

Low stringency hybridization refers to conditions equivalent tohybridization in 10% formamide, 5× Denhart's solution, 6×SSPE, 0.2% SDSat 22° C., followed by washing in 1×SSPE, 0.2% SDS, at 37° C. Denhart'ssolution contains 1% Ficoll, 1% polyvinylpyrolidone, and 1% bovine serumalbumin (BSA). 20×SSPE (sodium chloride, sodium phosphate, ethylenediamide tetraacetic acid (EDTA)) contains 3M sodium chloride, 0.2Msodium phosphate, and 0.025 M (EDTA). Other suitable moderate stringencyand high stringency hybridization buffers and conditions are well knownto those of skill in the art and are described, for example, in Sambrooket al., Molecular Cloning: A Laboratory Manual, 2nd ed., Cold SpringHarbor Press, Plainview, N.Y. (1989); and Ausubel et al., supra, 1999).Nucleic acid molecules encoding polypeptides hybridize under moderatelystringent or high stringency conditions to substantially the entiresequence, or substantial portions, for example, typically at least 15-30nucleotides of the nucleic acid sequences of cIAP2, TUCAN, Apaf1, Bcl-2,Smac, β-catenin or another biomarker.

The invention relates to the discovery that high or low amounts ofparticular biomarkers, including cIAP2, TUCAN, Apaf1, Bcl-2, β-cateninand Smac are predictive of survival of patients having cancer. Theover-expression or under-expression of these biomarkers can contributeto the genetic malfunction of cancer cells that leads to uncontrolledproliferation. Therefore, modulation of the level of a biomarker in acancer cell to a level consistent with a normal cell can be used toreturn a cancer cell to a more normal proliferation state. In the caseof over-expressed biomarker genes, such as cIAP2, TUCAN and β-catenin avariety of strategies can be employed to reduce gene expression. Forexample, inhibition of transcription or translation of cIAP2, TUCAN andβ-catenin, or reduction in the amount of active cIAP2, TUCAN andβ-catenin polypeptide, can be used to reduce the levels of thesebiomarkers to a level representative of a normal cell. In the case ofunder-expressed biomarker genes, such as Apaf1, Bcl-2 and Smac, avariety of strategies can be employed to increase gene expression. Forexample, introduction of Apaf1, Bcl-2 and Smac from an exogenous nucleicacid molecule, promotion of transcription or translation of Apaf1, Bcl-2or Smac, or promotion in the amount of active Apaf1, Bcl-2 or Smacpolypeptide, can be used to increase the levels of these biomarkers to alevel representative of a normal cell.

Therefore, the invention additionally provides a method for treating orreducing the progression of a neoplastic condition such as cancer byreducing neoplastic cell proliferation. In one embodiment, the methodinvolves administering a nucleic acid encoding Apaf1, Bcl-2 or Smac intoa neoplastic cell and expressing the Apaf1, Bcl-2 or Smac polypeptide inan amount effective to reduce neoplastic cell proliferation. In anotherembodiment, the method of reducing neoplastic cell proliferationinvolves contacting a neoplastic cell with an effective amount of anagent that, under sufficient conditions, increases the amount of Apaf1,Bcl-2 or Smac in the cell.

Such an agent can increase the amount of a biomarker directly orindirectly, for example, by increasing the amount of a biomarkerpolypeptide in a cell, such as by stimulating increased mRNA expression.Apaf1, Bcl-2 or Smac mRNA expression can be increased, for example, byinducing or derepressing transcription of Apaf1, Bcl-2 or Smac genes andby regulating the expression of a cellular protein that acts as atranscription factor to regulate gene expression. An agent can act toincrease the amount of Apaf1, Bcl-2 or Smac by increasing the stabilityof a Apaf1, Bcl-2 or Smac mRNA or polypeptide, for example, bydecreasing a cellular degradation activity, such as a protease activity.Molecules that mediate the regulation of Apaf1, Bcl-2 or Smacexpression, such as receptors and corresponding signal transductionmolecules, can also be targets of agents that increase the amount ofApaf1, Bcl-2 or Smac in a cell. For example, a signal transductionpathway that stimulates the expression of Apaf1, Bcl-2 or Smac can bemodulated to increase the level of Apaf1, Bcl-2 or Smac expression, forexample, by increasing the rate of Apaf1, Bcl-2 or Smac synthesis or thelength of time that gene expression remains active.

Conversely, a decrease in the amount of a biomarker in a cell can beaffected by inducing changes in biomarker transcription, translation orprotein stability opposite to those described above. As such, in afurther embodiment, the method of reducing neoplastic cell proliferationinvolves contacting a neoplastic cell with an effective amount of anagent that, under sufficient conditions, decreases the amount of cIAP2,β-catenin or TUCAN in the cell.

The amount of a biomarker in a cell, such as cIAP2, TUCAN, β-catenin,Bcl-2, Apaf1 or Smac, can be modulated, for example, by increasingexpression of the biomarker from an exogenous nucleic acid molecule, byintroducing a biomarker polypeptide or functional analog thereof into acell, by introducing inhibitor of a biomarker polypeptide into a cell,and by modulating the expression or activity of a gene or proteinproduct that regulates the level of a biomarker in a cell. The amount ofa biomarker in a cell also can be modulated using an antisense moleculeto block transcription or translation of the biomarker mRNA.Specifically, cells can be transformed with sequences complementary tocIAP2, β-catenin or TUCAN nucleic acid molecules. Such methods are wellknown in the art, and sense or antisense oligonucleotides or largerfragments, can be designed from various locations along the coding orcontrol regions of sequences encoding biomarkers. Thus, antisensemolecules can be used to modulate biomarker activity, or to achieveregulation of gene function.

Ribozymes, enzymatic RNA molecules, can also be used to catalyze thespecific cleavage of a biomarker mRNA, such as cIAP2, β-catenin orTUCAN. The mechanism of ribozyme action involves sequence-specifichybridization of the ribozyme molecule to complementary target biomarkerRNA, followed by endonucleolytic cleavage. Specific ribozyme cleavagesites within any potential RNA target are identified by scanning thebiomarker RNA for ribozyme cleavage sites which include the followingsequences: GUA, GUU, and GUC. Once identified, short RNA sequences ofbetween 15 and 20 ribonucleotides corresponding to the region of thetarget gene containing the cleavage site can be evaluated for secondarystructural features which can render the oligonucleotide inoperable. Thesuitability of candidate targets can also be evaluated by testingaccessibility to hybridization with complementary oligonucleotides usingribonuclease protection assays. Antisense molecules and ribozymes of theinvention can be prepared by any method known in the art for thesynthesis of nucleic acid molecules.

RNA interference (RNAi) can also be used to modulate the amount of abiomarker mRNA, such as cIAP2, β-catenin or TUCAN. RNAi is a process ofsequence-specific gene silencing by post-transcriptional RNAdegradation, which is initiated by double-stranded RNA (dsRNA)homologous in sequence to the silenced gene. A suitable double-strandedRNA (dsRNA) for RNAi contains sense and antisense strands of about 21contiguous nucleotides corresponding to the gene to be targeted thatform 19 RNA base pairs, leaving overhangs of two nucleotides at each 3′end (Elbashir et al., Nature 411:494-498 (2001); Bass, Nature411:428-429 (2001); Zamore, Nat. Struct. Biol. 8:746-750 (2001)). dsRNAsof about 25-30 nucleotides have also been used successfully for RNAi(Karabinos et al., Proc. Natl. Acad. Sci. 98:7863-7868 (2001). dsRNA canbe synthesized in vitro and introduced into a cell by methods known inthe art.

A variety of methods are known in the art for introducing a nucleic acidmolecule into a cell, including a cancer cell. Such methods includemicroinjection, electroporation, lipofection, calcium-phosphate mediatedtransfection, DEAE-Dextran-mediated transfection, polybrene- orpolylysine-mediated transfection, and conjugation to an antibody,gramacidinS, artificial viral envelopes or other intracellular carrierssuch as TAT. For example, cells can be transformed by microinjection asdescribed in Cibelli et al., Nat. Biotech. 16:642-646 (1998) or Lamb andGearhart, Cur. Opin. Gen. Dev. 5:342-348 (1995); by lipofection asdescribed in Choi (U.S. Pat. No. 6,069,010) or Lamb and Gearhart, Cur.Opin. Gen. Dev. 5:342-348 (1995); by electroporation as described inCurrent Protocols in Molecular Biology, John Wiley and Sons, pp9.16.4-9.16.11 (2000) or Cibelli et al., Nat. Biotech. 16:642-646(1998); or by fusion with yeast spheroplasts Lamb and Gearhart, Cur.Opin. Gen. Dev. 5:342-348 (1995).

A nucleic acid encoding a biomarker polypeptide, such as Apaf1, Bcl-2 orSmac, or other molecule useful for reducing proliferation of a cancercell, can be delivered into a mammalian cell, either in vivo or in vitrousing suitable vectors well-known in the art. Suitable vectors fordelivering a nucleic acid encoding a biomarker polypeptide to amammalian cell, include viral vectors and non-viral vectors such asplasmid vectors. Such vectors are useful for providing therapeuticamounts of a biomarker polypeptide, such as Apaf1, Bcl-2 or Smac, aswell as for delivering antisense nucleic acid molecules and ribozymes.

Viral based systems provide the advantage of being able to introducerelatively high levels of the heterologous nucleic acid into a varietyof cells. Suitable viral vectors for introducing a nucleic acid encodinga biomarker polypeptide, such as Bcl-2, Smac or Apaf1, into a mammaliancell are well known in the art. These viral vectors include, forexample, Herpes simplex virus vectors (Geller et al., Science,241:1667-1669 (1988)); vaccinia virus vectors (Piccini et al., Meth.Enzymology, 153:545-563 (1987)); cytomegalovirus vectors (Mocarski etal., in Viral Vectors, Y. Gluzman and S. H. Hughes, Eds., Cold SpringHarbor Laboratory, Cold Spring Harbor, N.Y., 1988, pp. 78-84)); Moloneymurine leukemia virus vectors (Danos et al., Proc. Natl. Acad. Sci. USA,85:6460-6464 (1988); Blaese et al., Science, 270:475-479 (1995); Onoderaet al., J. Virol., 72:1769-1774 (1998)); adenovirus vectors (Berkner,Biotechniques, 6:616-626 (1988); Cotten et al., Proc. Natl. Acad. Sci.USA, 89:6094-6098 (1992); Graham et al., Meth. Mol. Biol., 7:109-127(1991); Li et al., Human Gene Therapy, 4:403-409 (1993); Zabner et al.,Nature Genetics, 6:75-83 (1994)); adeno-associated virus vectors(Goldman et al., Human Gene Therapy, 10:2261-2268 (1997); Greelish etal., Nature Med., 5:439-443 (1999); Wang et al., Proc. Natl. Acad. Sci.USA, 96:3906-3910 (1999); Snyder et al., Nature Med., 5:64-70 (1999);Herzog et al., Nature Med., 5:56-63 (1999)); retrovirus vectors (Donahueet al., Nature Med., 4:181-186 (1998); Shackleford et al., Proc. Natl.Acad. Sci. USA, 85:9655-9659 (1988); U.S. Pat. Nos. 4,405,712, 4,650,764and 5,252,479, and WIPO publications WO 92/07573, WO 90/06997, WO89/05345, WO 92/05266 and WO 92/14829; and lentivirus vectors (Kafri etal., Nature Genetics, 17:314-317 (1997)). It is understood that bothpermanent and transient expression can be useful in a method of theinvention.

An Apaf1, Bcl-2 or Smac polypeptide-encoding recombinant nucleic acidcan be directed into a particular tissue or organ system, for example,by vector targeting or tissue-restricted gene expression. Therefore, avector useful for therapeutic administration of a nucleic acid encodingan Apaf1, Bcl-2 or Smac polypeptide can contain a regulatory elementthat provides tissue specific expression of the polypeptide. Forexample, a nucleic acid sequence encoding a Apaf1, Bcl-2 or Smacpolypeptide can be operatively linked to a cell specific promoter.

Any of a variety of inducible promoters or enhancers can also beincluded in a nucleic acid or vector of the invention to allow controlof expression of a Apaf1, Bcl-2 or Smac polypeptide, or another moleculeuseful for modulating cell proliferation, such as an antisense nucleicacid molecule or ribozyme, by added stimuli or molecules. Such induciblesystems, include, for example, tetracycline inducible system (Gossen &Bizard, Proc. Natl. Acad. Sci. USA, 89:5547-5551 (1992); Gossen et al.,Science, 268:1766-1769 (1995); Clontech, Palo Alto, Calif.);metalothionein promoter induced by heavy metals; insect steroid hormoneresponsive to ecdysone or related steroids such as muristerone (No etal., Proc. Natl. Acad. Sci. USA, 93:3346-3351 (1996); Yao et al.,Nature, 366:476-479 (1993); Invitrogen, Carlsbad, Calif.); mouse mammarytumor virus (MMTV) induced by steroids such as glucocorticoid andestrogen (Lee et al., Nature, 294:228-232 (1981); and heat shockpromoters inducible by temperature changes.

An inducible system particularly useful for therapeutic administrationutilizes an inducible promoter that can be regulated to deliver a levelof therapeutic product in response to a given level of drug administeredto an individual and to have little or no expression of the therapeuticproduct in the absence of the drug. One such system utilizes a Gal4fusion that is inducible by an antiprogestin such as mifepristone in amodified adenovirus vector (Burien et al., Proc. Natl. Acad. Sci. USA,96:355-360 (1999). The GENE SWITCH inducible expression system (U.S.Pat. Nos. 5,935,934 and 5,874,534) is an example of such a system. Otherinducible systems use the drug rapamycin to induce reconstitution of atranscriptional activator containing rapamycin binding domains of FKBP12and FRAP in an adeno-associated virus vector (Ye et al., Science,283:88-91 (1999)), use tetracycline to control transcription (BaronNucleic Acids Res. 25:2723-2729 (1997)) and use synthetic dimerizers toregulate gene expression (Pollock et al., Methods Enzymol. 306:263-281(1999)). Such a regulatable inducible system is advantageous because thelevel of expression of the therapeutic product can be controlled by theamount of drug administered to the individual or, if desired, expressionof the therapeutic product can be terminated by stopping administrationof the drug.

It is understood that modifications which do not substantially affectthe activity of the various embodiments of this invention are alsoincluded within the definition of the invention provided herein.Accordingly, the following examples are intended to illustrate but notlimit the present invention.

EXAMPLE I Generation of Antibodies for Immunodetection of IAPs and Apaf1

This example shows preparation and characterization of antibodies usefulfor detecting IAPs and Apaf1.

Antisera were raised against recombinant proteins and synthetic peptidesfor immunodetection of Survivin, XIAP, Apaf1, AIF and Smac. Prior toemploying these antibodies for analysis of cancers, the specificity ofthese antibodies for their intended protein targets was confirmed bySDS-PAGE/immunoblot analysis. Examples of data are provided in FIG. 3.FIG. 3A shows in vitro translated Survivin, XIAP, cIAP1, cIAP2, NAIP,BRUCE, and baculovirus Cp-IAP proteins were subjected toSDS-PAGE/immunoblot analysis, using polyclonal XIAP antiserum (AR-27A).Incubation with XIAP antiserum detected only XIAP in vitro translatedprotein. Detergent lysates were prepared from various normal humantissues, normalized for total protein content (50 ug), and subjected toSDS-PAGE/immunoblot assay using antisera specific for Survivin (B),Apaf1 (C), SMAC (D) or AIF (E); molecular weight markers are indicatedin kilo-Daltons (F). In addition, lysates from 5 matched pairs of coloncarcinoma (T) and normal colonic mucosa (N) specimens were analyzed fortotal protein content (100 mg per lane) and subjected toSDS-PAGE/immunoblot analysis, using the antisera specific for c-IAP1,c-IAP2, XIAP, Survivin, Apaf-1, and TUCAN (G). Antibody detection wasaccomplished by an ECL method. Immunoblot data were quantified byscanning densitometry using Pro-Image software system.

The anti-XIAP antiserum reacted specifically with the expected ˜57 kDaXIAP protein, but not with other IAP-family members including Survivin,cIAP1, cIAP2, NAIP, BRUCE, and baculovirus Cp-IAP—which were allproduced by in vitro transcription and translation from cDNAs (FIG. 3A).Similarly, monospecificity of the anti-Survivin antiserum wasdemonstrated by SDS-PAGE/immunoblot analysis of recombinant IAP-familyproteins and lysates from normal tissues which lack Survivin mRNA andprotein versus tumor cell lines which express Survivin protein (FIG.3B). The anti-Smac antiserum displayed specific reactivity againstGST-Smac recombinant protein (FIG. 3C). The antibody detected abundantamounts of Smac protein in RS11 and Jurkat cells, and several humantissues, such as epidermis, brain and testis. Barely detectable Smaclevels in normal colon contrasted with relatively high amount of thisprotein in a colon cancer lysate.

Polyclonal antisera for Survivin, Apaf1, XIAP and Smac were generated inrabbits using recombinant protein immunogens. Survivin (full-lengthprotein) and Apaf1 (residues 264-282) were produced as GST-fusionproteins from pGEX vectors using Escherichia coli BL21 (DE3) as the hoststrain. The protein purification method has been described previously.An additional anti-Apaf-1 serum was generated in rabbits using asynthetic peptide as the immunogen. A peptide(NH2-CGPKYVVPVESSLGKEKGLE-amide (SEQ ID NO:15)) corresponding toresidues 264-282 of human (hu) Apaf-1, was synthesized with anN-terminal cysteine appended to permit conjugation tomaleimide-activated carrier proteins KLH and OVA (Pierce, Inc.). Thispeptide conjugate was used to generate a polyclonal antiserum (AR-23) inrabbits. Affinity-purified His 6-tagged—XIAP BIR2 recombinant proteinwas produced using published methods and was used as an immunogen toproduce XIAP-specific antiserum (AR-27A). An anti-AIF serum was producedin rabbits using a synthetic peptide corresponding to residues 151-170of human AIF. New Zealand white female rabbits were injectedsubcutaneously with a mixture of 0.25 ml KLH-peptide (1 mg/ml), 0.25 mlOVA-peptide (1 mg/ml), or recombinant protein (0.1-0.25 μg protein perimmunization) and 0.5 ml Freund's complete adjuvant (dose divided over10 injections sites) and then boosted 3 times at weekly intervals,followed by another 3-20 boostings at monthly intervals with 0.25 mgeach of KLH-peptide, OVA-peptide, or recombinant protein immunogens inFreund's incomplete adjuvant, collecting blood at 1-3 weeks after eachboosting to obtain immune serum. The generation of Bcl-2, Bcl-XL, Bax,and TUCAN-specific antisera has been described. Anti-c-IAP1 and c-IAP2antibodies were obtained from Santa Cruz Biotechnology Inc., CA and R&DSystems, Inc., β-Catenin Laboratories, and p53 clone DO-7, MIB-1, andBAG1 clone KS-6C8 from DAKO.

EXAMPLE II Immunohistochemical Analysis of IAPs and Other Biomarkers inNormal Colonic Mucosa and Colon Cancer

This example shows immunohistochemical analysis of IAPs and otherbiomarkers in a tissue microarray representing tissue samples obtainedfrom 102 individuals.

A tissue microarray was constructed using primary tumor specimensderived from a relatively homogenous cohort of 102 patients presentingwith stage II disease (Dukes' B stage) to a single institution, and whowere treated by surgical resection with curative intent. Colon carcinomaspecimens were obtained from Department of Pathology, Yonsei University,College of Medicine, Seoul, Korea. Samples were taken from primarytumors derived from patients who presented between 1986 and 1996 withDukes' B stage [stage II disease, as defined by American Joint Committeeon Cancer and Union Internationale Contre le Cancer (AJCC/UICC)criteria]. Patients with Dukes' stage B2 (T3N0M0) constituted 91% of thecohort, whereas 9% suffered from a Dukes′B3 (T4N0M0) cancer. Allpatients were treated by surgical resection of the involved segment ofcolon. No postoperative adjuvant chemotherapy was performed initially inall cases. However, chemotherapy was administered for some patientsafter relapse. Clinical data represent a median follow up of 60 months.

To construct colon cancer microarrays, 2-5 cylinders of 1 mm diametertissue were taken from representative areas of archival paraffin blockscontaining 8% formalin-fixed tumor and arrayed into a new recipientparaffin block with a custom-built precision instrument (BeecherInstruments, Silver Spring, Md.). Serial sections (4 ÿm) were applied to3-aminopropyl-triethoxysilane (APES)-coated slides (Sigma).

Microarrays were immunostained using antisera specific for the IAPfamily members Survivin, XIAP, cIAP1, and cIAP2 (FIG. 1A), and othermarkers such as Apaf1, Smac, AIF, Bcl-2, Bcl-XL, Bax, BAG1, β-Catenin,MIB-1 and p53. Dewaxed tissue sections were immunostained using adiaminobenzidine (DAB)-based detection method as described in detail,employing the Envision-Plus-Horse Radish Peroxidase (HRP) system (DAKO)using an automated immunostainer (Dako Universal Staining System).Antisera specific for Survivin, XIAP, Apaf1, TUCAN, AIF, Smac, Bax, andBid were applied at 1:3000 to 10000 (v/v), for Bcl-2 and Bcl-XL at1:2000 (v/v). The dilutions of c-IAP1, c-IAP2 and β-Catenin antibodieswere 1:600 (v/v), BAG1 and MIB-1 1:100, and p53 1:50. For all tissuesexamined, the immunostaining procedure was performed in parallel usingpreimmune serum to verify specificity of the results. Initialconfirmations of antibody specificity also included experiments in whichantiserum was preabsorbed with 5-10 ÿg/ml of either synthetic peptideimmunogen or recombinant protein immunogen. The scoring of tumorimmunostaining was based on the percentage of immunopositive cells(0-100) multiplied by staining intensity score (0/1/2/3), yieldingscores of 0-300. All immunostaining results were quantified accordingthe approximate percentage of immunopositive cells (0-100%) andimmunointensity on a 0-3 scale, and then an immunoscore was calculatedfrom the product of the percentage immunopositivity and immunointensity(0-300).

Tissue sections were immunostained using various antisera, as describedabove, followed by detection using a HRPase-based method withdiaminobenzidine colorimetric substrate (brown). Nuclei werecounterstained with hematoxylin (blue). Representative data are shown inFIG. 1. FIG. 1A shows a colon cancer microarray slide stained for cIAP2(×5 magnification). Examples of normal colonic epithelium immunostainingare presented for cIAP1 (B; ×100), Survivin (D; ×150), Smac (E; ×150),AIF (G; ×150), and Tucan (K; ×20). Immunostaining results in regions ofinvasive cancer are shown for Smac (F; ×400), AIF (H; ×250), Apaf1 (I,J; ×200), TUCAN (L ×20; M ×400), and Bcl-2 (N; ×150). Examples ofmalignant and the adjacent normal colonic epithelium are presented forcIAP2 (C; ×40), p53 (O; ×150) and MIB-1 (P; ×400).

Several of the 102 tumor specimens on the array (˜65%) containedadjacent normal colonic mucosa (59-70), depending on the particularslide), permitting side-by-side comparisons of immunostaining resultsfor normal versus malignant epithelium. In addition, 4 specimens ofnormal colon derived from individuals who were not diagnosed with coloncancer were stained separately. Immunoreactivity for the cIAP1 and cIAP2proteins was detected in 62/62 (100%) and 34/65 (52%) of normal colonicmucosa specimens examined, respectively. The intensity of cIAP1 stainingin non-malignant epithelium progressively increased from the base of thecrypts to the luminal surface (FIG. 1B). In contrast, low cIAP2immunoreactivity was more evenly distributed along the crypt-villusaxis, though a slight increase in immunointensity in the upper regionsof the villi was sometimes evident. XIAP was also expressed innon-malignant colonic epithelium (63/63 [100%]) and was distributed in agradient similar to cIAP1, with XIAP immunoreactivity highest in theupper portions of the villi. Low intensity Survivin immunostaining waspresent in 60/62 (97%) of specimens containing normal colonicepithelium. Survivin immunoreactivity was predominantly nuclear in thecrypt epithelial cells, and became progressively stronger in intensityand predominantly cytoplasmic towards the luminal surface along thecrypt-villus axis (FIG. 1D). Immunohistochemical analysis of Apaf1 innormal colonic mucosa revealed the presence of immunoreactivity in 58/60(97%) of specimens. Apaf1 immunoreactivity was present predominantly inperi-nuclear and cytosolic locations in normal colonic epithelial cells,with the intensity slightly increasing as the cells migrated from thecrypt bases to the upper regions of the villi. Along with Apaf1, theintracellular concentration of Smac protein increased towards theluminal surface in 58/62 (94%) of normal colonic mucosa specimens (FIG.1E). A relatively high mostly granular cytosolic expression of AIF wasuniformly distributed along the colonic crypts in 100% of specimens(60/60) (FIG. 1G). The specificity of these immunostaining results wasconfirmed by control stainings performed using either preimmune serum orimmune antisera which had been preabsorbed with the relevant immunogens.

Immunohistochemical analysis of tumor tissues on the microarray revealedseveral examples of cancer-specific alterations in the expression ofthese apoptosis-regulatory proteins. FIG. 1 shows some examples of theimmunostaining results for tumor specimens. The mean intensity ofimmunostaining was significantly higher in the invasive cancer comparedto normal colonic epithelium for all investigated proteins (FIG. 1C,E-F, K-M, 0, P) with the exception of Bcl-2, Bax, and AIF (FIG. 1G, H).Moreover, while immunostaining results varied widely among specimensexamined, the immunoscores for a portion of the cancer specimensclustered into groups displaying clear elevations in immunoreactivitywhen compared to normal specimens (FIG. 2). For example, while allnormal colonic specimens had cIAP1 immunoscores of <200, 35 of 94 (37%)invasive cancer specimens had immunoscores of=200 (p<0.0001), thussuggesting that a subgroup of colon cancers develops pathologicalelevations in the levels of this anti-apoptotic protein. Similarly,cIAP2 immunoscores were <100 for normal colonic epithelium, in contrastto invasive cancers where 25 of 94 (27%) had immunoscores of >100(p<0.0001). Likewise, all normal colonic epithelium samples possessedimmunoscores of <190 for XIAP, while 34 of 97 invasive cancers (35%) hadXIAP immunoscores of >190 (p<0.0001). Survivin immunoscores fornon-malignant epithelium were <190, compared to invasive cancers wherescores >190 were found for 33 of 100 (33%) of specimens (p<0.0001). ForApaf1, two clusters of immunoscores emerged for both normal andmalignant epithelium. Most normal colonic specimens (50/60; 83%) hadimmunoscores <100. In tumors, a group of specimens with similarly lowimmunoscores (<100) was observed (64/102; 63%) (FIG. 1J) but anadditional group of cancers (38/102; 37%) was identified in whichimmunoscores clustered above 100, ranging from 140-280 (FIG. 1I). Theseresults show that for all biomarkers examined, evidence oftumor-associated upregulation of expression was observed in a portion ofthe cancer specimens evaluated.

Elevated levels of cIAP2, Survivin, and β-Catenin correlated with highKi-67 labeling index (p=0.006, p=0.005, and p=<0.0001, respectively).Statistical analysis revealed a significant correlation between thelevels of Survivin and those of XIAP and cIAP1 (p=0.01), or cIAP2(p=0.008). Elevated levels of survivin were associated with highexpression of Bcl-2. A positive correlation between the expression ofcIAP2 and TUCAN (p=0.003) agrees with an observed positive impact that acombination of low levels of these proteins has on survival in ourcohort of colon carcinoma patients. However, an inverse correlationbetween TUCAN and Bcl-2 or AIF, did not reach a statisticalsignificance. No significant association between cIAP2 and Apaf1 orBcl-2 was found. Bcl-2, which has implications of a good prognosticmarker in our cohort, correlates significantly with some pro-apoptoticproteins, such as Apaf1 (p<0.0001), AIF (p=0.002) and Smac (p=0.008),but also with an anti-apoptotic BAG1 protein which was found to predictlong-term survival in early-stage breast cancer (#7874). An increasednuclear concentration of p53, which in 80% is related to p53 pointmissense mutation correlated with increased expression of Bcl-XL(p<0.0001).

EXAMPLE III Immunoblot Analysis of IAPs and Apaf1 in Colon Carcinoma

This example shows immunoblot analysis of IAPs, Apaf1 and otherapoptosis-regulators in five frozen colon cancer specimens.

To corroborate the immunohistochemistry data, five frozen colon cancerspecimens were identified that had sufficient amounts of both adjacentnormal (N) and tumor (T) tissue for immunoblot analysis using antibodiesspecific for IAPs, Apaf1, and other proteins. Detergent-lysates of thesetissues specimens were prepared and normalized for total protein contentprior SDS-PAGE/immunoblot analysis (FIG. 3E). Densitometry analysis wasalso performed to quantify band intensities, and the results from theloading control blot were used to normalize all data (FIG. 3F).

Colon cancer specimens (n=10) with high ratios of cancer cells relativeto stroma (>70%) were selected for immunoblotting analysis. The proteinlysates were prepared without additional microdissection orfractionation. The tumor lysates and the samples of the normal mucosafrom the same patients were prepared using modified RIPA buffer (50 mMTris [pH 7.4], 150 mM NaCl, 0.25% Na-deoxycholate, 1% NP40, 1 mM EDTA, 1mM Na3VO4, 1 mM NaF, 1 mM PMSF) containing complete protease inhibitorcocktail (SIGMA), Pan-Caspase inhibitorz-Asp-2.6-dichlorobenzoyloxy-methylketone and ZVAD-fmk, normalized fortotal protein content (100 ug) and resolved by SDS-PAGE (12% and 15%gels). Protein quantification was performed using the Bio-Rad ProteinAssay Kit (Bio-Rad). Proteins were transferred (overnight 150 mA, 4° C.)to PVDF membranes (Amersham Pharmacia). After blocking with 5% skim milkin TBST (50 mM Tris [pH 7.6], 150 mM NaCl, 0.05% Tween 20) at roomtemperature for 2 hours, blots were incubated overnight with antiseraspecific for particular IAP family members, Apaf1, and TUCAN, using1:1,000-1:10,000 (v/v) dilutions at 4° C. After incubation withHRPase-conjugated secondary goat anti-rabbit (either Bio-Rad or SantaCruz) antibody at room temperature for 1 hr, immunodetection wasaccomplished by an enhanced chemoluminescence (ECL) method (Amersham),with exposure to x-ray film (Kodak/XAR). Densitometry was performed toquantify the intensity of bands, using Image-pro Plus software.

Higher levels of cIAP2, XIAP, Survivin, and Apaf1 were detected in everyspecimen evaluated, compared to case-matched normal tissue. Levels ofcIAP1 protein, as well as the anti-apoptotic protein TUCAN, wereelevated in some tumor specimens compared to normal, but not others. Anonspecific band obtained during preblocking procedure with a secondaryECL antibody (Biorad), served as a loading control.

The immunoblotting results confirmed the immunohistochemistryobservations described in Example II (FIG. 1 E, F). Higher levels ofcIAP2, XIAP, Survivin, and Apaf1 were detected in every specimenevaluated as compared to case-matched normal tissue. Levels of cIAP1 andTUCAN were elevated in some tumor specimens compared to normal, but notothers.

EXAMPLE IV Correlation of Protein Expression with Clinical Outcome

To analyze the relation of biomarkers with patient survival, thecomparisons of the immunoscores obtained for normal colonic epitheliumand colon cancers shown in FIG. 3 were used to set logical cut-offs fordichotomization of data.

Clinical data were available for all patient specimens included on thetissue microarray with respect to relapse and overall survival, with amedian follow-up of 5 years. Patients were categorized as: (i) Alivewithout disease (A); (ii) Alive with recurrent disease (R); or (iii)Dead (D). As shown in Table 2, no significant differences in theimmunoscores for cIAP1, XIAP, or Survivin were observed when comparingthe A, R, and D groups of patients. cIAP2 immunostaining wassignificantly higher in colon cancer patients who had either died ofdisease (D) or who had relapsed after surgery (R) (p<0.0001). Incontrast, immunoscores for Apaf1 were significantly lower in the groupsof patients who had relapsed (R) or died (D), compared to patients whowere alive without disease (p<0.0001) (Table 2). An unpaired t-testmethod was used for comparisons of XIAP, Survivin, cIAP1, cIAP2, andApaf1 immunoscores in the A, R, and D groups of patients. P-values referto a comparison of group A with the combined groups R and D. TABLE 2Summary of immunostaining results for colon cancer patients PatientSurvivin cIAP1 cIAP2 Apaf1 Status Mean ± SE Median Mean ± SE Median Mean± SE Median Mean ± SE Median Mean ± SE Mean A 168 ± 9  160 152 ± 10 160166 ± 9  190 81 ± 9 70 132 ± 8  140 R 176 ± 22 170 125 ± 25 95 169 ± 23180 162 ± 22 160 46 ± 20 50 D 177 ± 13 180 135 ± 13 120 174 ± 13 180 146± 13 130 77 ± 12 70 p-values 0.5 0.2 0.7 <.0001 <.0001 A vs R+D

To analyze the relation of biomarkers to patient survival by anothermethod, immunostaining data for these proteins were dichotomized intohigh- versus low-expression groups. For this purpose, the comparisons ofthe immunoscores obtained for normal colonic epithelium and coloncancers shown in FIG. 2 were used to set logical cut-offs forichotomization of data. Immunoscores for normal and malignant colonepithelium were depicted in a graphic form in FIG. 2. Based oncomparisons with normal colonic epithelium, cutoffs for dichotomizingimmunostaining data were selected. The range of immunoscore for 95% ofnormal specimens defined a group of tumors with low immunoscore forcIAP1, cIAP2, XIAP, Survivin, Bcl-XL, and BAG1. Bimodal distribution ofproteins helped to identify cut-offs for Bax, Apaf1 and TUCAN. Theapplication of median immunoscores as cut-offs for Bcl-2, Bid, AIF,Smac, and β-catenin, increased accuracy in the subcategorisation oftumors into low and high expressors. The histograms for p53 and MIB-1present the immunopercentage, classifying cases >0% as high p53expressors and=20% as those expressing high levels of MIB.

Based on this method, high levels of Apaf1, TUCAN, Survivin, XIAP,cIAP1, and cIAP2 were found in 38%, 49%, 54%, 74%, 61% and 35% tumorspecimens, respectively. In univariate analysis, significantcorrelations were observed in this cohort between longer disease-freesurvival (DFS) and low expression of cIAP2 (p=0.0002), TUCAN (p=0.0004),β-Catenin (p=0.04), mutant p53 protein (p=0.03), or high levels of Apaf1(p=0.00008), Bcl-2 (p=0.005), and SMAC (p=0.03) (FIG. 4 a). Thus, 78%(39/50) of patients whose tumors contained low levels of TUCAN remainedalive and disease-free during the time covered by this study, comparedto only 44% (21/48) of those with high expression of this protein.Similarly, 74% (45/61) of low cIAP2 expressors enjoyed colon cancer-freelife at the time of last survey compared to only 36% (12/33) of thosewith high cIAP2 levels. In contrast, high levels of Apaf1 wereassociated with longer survival in this cohort of colon cancer patients,with 33/38 (87%) of patients remaining disease-free compared to only28/62 (45%) of those with low Apaf1 expression.

The most significant improvement of overall survival was noticed in agroup of patients whose colon carcinoma specimens contained low levelsof TUCAN (p<0.0001) (FIG. 4 b). Among 50 patients expressing low TUCAN,only 4% (2/50) died, as opposed to 54% (26/48) of those presenting highlevels of this protein. Significant correlations were also observedbetween longer overall survival and low cIAP2 (p=0.01) or low mutant p53protein (p=0.03). Low Bcl-2 levels were associated with poor overallsurvival. Of 18 patients with low expression of this protein, 11 (61%)died of colon cancer, compared with 24% of patients who died in thehigh-Bcl-2 group (18/76). Similarly, patients whose tumors contained lowApaf1 staining had worse overall survival compared with those whooverexpressed Bcl-2 (FIG. 1N).

Elevated levels of Bcl-2 conferred a significant advantage for bothoverall (p=0.0008) and disease-free survival (p=0.005). Of 76 patientswhose tumors revealed high Bcl-2, 58 (76%) remained alive and 50 (66%)relapse-free, compared to 39% and 33% of those with low Bcl-2 levels.Independent of its anti-apoptotic function, Bcl-2 can delay entry intothe cell cycle and promote exit of cells from the cycle. Thus, apositive effect of Bcl-2 on clinical outcome may be linked to its cellcycle-inhibitory role.

FIG. 4 shows correlations of biomarkers immunostaining data withdisease-free (A) survival and overall survival (B) for colon carcinomapatients. All biomarkers data and outcome measures were entered into adatabase using STATISTICA software system (StatSoft). The log rank testwas used to for correlation of immunoscore data with the patientsurvival. The Kaplan-Meier curves illustrate correlations of theinvestigated biomarkers with survival for this cohort of patients.

In summary, at a median follow-up of 5 years, 60% of patients with highcIAP2 levels relapsed and 46% died of colon cancer, whereas in alow-cIAP2 group there were 20% relapses and 18% colon cancer-relateddeaths. At the same time point, 49% of patients with high expression ofTUCAN had relapse or died of colon cancer, and only 19% had recurrenceand 4% died of disease in a group of patients whose tumors expressed lowlevels of this protein. In contrast, 43% of patients with low Apaf1relapsed and 35% died of colon cancer, while only 14% had a cancerrecurrence or died in a high-Apaf1 cohort. Thus, these findings indicatethat higher levels of the anti-apoptotic protein cIAP2 and lower levelsof the pro-apoptotic protein Apaf1 are associated with adverse outcomein patients with early-stage colon cancer. No significant differenceswere noted in the age, or gender of the patients in the high- versuslow-expression groups for cIAP2, Apaf1, or TUCAN.

EXAMPLE V Combined Analysis of cIAP2 and Apaf1 Expression Data

This example shows combined analysis of cIAP2 and Apaf1 expression data.

Since certain proteins had significant prognostic value, it wasdetermined whether combining two biomarkers could identify a subgroup ofpatients with distinct survival characteristics. Patients with twofavorable variables (low cIAP2 and high Apaf1) were compared with allother patients in this cohort.

FIG. 5 shows correlations of biomarkers and their combinations withdisease-free (A, B) survival and overall survival (C, D) for coloncarcinoma patients. Using the Kaplan-Meier curves, panels A and Billustrate a combination of biomarkers [low cIAP2 and high Apaf1 (A);low cIAP2 and low TUCAN (B)] with positive impact on disease-freesurvival. The two combinations of markers with an adverse effect onsurvival are presented in panel C (low Apaf1 and high TUCAN), and panelD (low Bcl-2 and high cIAP2).

Of the 94 patient samples successfully analyzed for cIAP2 and Apaf1, 25(27%) had both a low cIAP2 and a high Apaf1 immunoscore. All (100%) ofthese patients with the combination of low cIAP2 and high Apaf1 werealive and disease-free at 5 years after diagnosis, compared to 52%disease-free or 64% alive of others (p=0.00007 for OS; p<0.0001 for DFS)(FIG. 5A; DFS). At the same time point, among patients with acombination of low cIAP2 and low TUCAN 97% were alive and 94%disease-free, compared to 59% alive and 50% disease-free of others(p=0.00001) (FIG. 5B; DFS). Thus, the combinations of cIAP2 and Apaf1 orcIAP2 and TUCAN immunostaining data identify a subgroup of colon cancerpatients with distinct survival characteristics. However, when patientswith two adverse biomarkers (low Apaf1 and high TUCAN) were comparedwith other patients, 34% of patients with this combination of proteinsand 90% of others were alive at 5 years after diagnosis (p<0.0001) (FIG.4C). The discrepancy was even larger at the end of the survey, with 0%and 90% of those who remained alive, respectively. When combination datawere examined for another pair of adverse biomarkers (cIAP2 high andBcl-2 low), none of the patients was alive in this group 5 years aftersurgery, but 75% of others survived (p=004) (FIG. 4D). These results arein agreement with an outcome of the LERS data analysis.

EXAMPLE VI Multivariate Analysis Identifies cIAP2, Apaf1, TUCAN andBcl-2 as Independent Prognostic Indicators of Survival in Early-StageColon Cancer

This example shows that multivariate analysis confirms that cIAP2,Apaf1, TUCAN and Bcl-2 are independent prognostic indicators of survivalin early-stage colon cancer.

Multivariate Cox proportional hazards models were fitted to assesswhether elevated levels of biomarkers were associated with disease-freesurvival (DFS) and overall survival (OS). The variables were notstratified into T3N0M0 and T4N0M0 subgroups due to a small number ofpatients involved in this study. In addition, the data mining systemLERS (Learning from Examples based on Rough Sets) was employed toperform a multivariate analysis of immunohistochemical staining data.

In this project, the algorithms LEM2 was determined to be the mostapplicable to the data and therefore was employed for multivariateanalysis. The presence of high cIAP2 and high TUCAN increased risk ofdeath from colon cancer within this cohort of patients 2.7-fold (p=0.01)and 17-fold ((p=000004), respectively. High Apaf1 and Bcl-2 expressionwas associated with a decreased relative risk of dying of colon cancerby 75% (p=0.004) and 82% (p=0.00006).

When an association of protein levels with disease-free survival wasassessed by multivariate analysis, cIAP2 and TUCAN maintained prognosticsignificance (p=0.000005, p=0.0005), with high levels of these proteinsincreasing risk of recurrence 6-fold and 3.4-fold, respectively. AlsoApaf1 and Bcl-2 retained their significant prognostic role (p=0.006,p=0.0004), decreasing the hazard rate of colon cancer recurrence byapproximately 75%. Additionally, high levels of Smac decreased the riskof recurrence by 63%. No role of Smac was evident for overall survivalof patients in this cohort. Taken together, these findings indicate thatimmunostaining data for cIAP2, Apaf1, TUCAN, Bcl-2 and their combinationcan have prognostic significance for patients with early-stage coloncancer. Table 3 A-B shows multivariate analysis of DFS (A) and OS (B) instage II colon carcinoma patients using backward stepwise Coxproportional hazards regression analysis to assess whether elevatedlevels of biomarkers were associated with disease-free survival oroverall survival. TABLE 3 Multivariate analysis of DFS (A) and OS (B) instage II colon carcinoma patients HR BIOMARKER coeffifcient (95% CI) pA. DFS cIAP2 1.79  5.96 0.000005 (2.78 - 12.8) Apaf1 − 1.27  0.28 0.006(0.11 - 0.68) TUCAN 1.23  3.43 0.0005  (1.6 - 6.55) Bcl-2 − 1.37  0.250.007 (0.13 - 0.60) Smac − 1.00  0.37 0.007 (0.19 - 0.81) B. OS cIAP20.98  2.66 0.01 (1.04 - 5.42) Apaf1 − 1.36  0.26 0.004 (0.10 - 0.65)TUCAN 2.84 17.19 0.000004  (5.12 - 57.48) Bcl-2 − 1.69  0.18 0.00006(0.08 - 0.43)

EXAMPLE VII Expression of TUCAN in Multiple Cancer Cell Lines

This example shows that TUCAN is expressed in several tumor cell lines.

To determine the expression of TUCAN in cancers, the NCI panel of 60human tumor cell lines (Weinstein, et al. Science 17:343-349 (1997)) wasanalyzed by immuno-blotting using an antiserum specific for TUCAN (FIG.6A). Cell lines included in the panel are shown in Table 4, below: TABLE4 NCI panel of 60 human tumor cell lines Cell Line Name Cell TypeCCRF-CEM Leukemia HL-60 (TB) Leukemia K-562 Leukemia MOLT-4 LeukemiaRPMI-8226 Leukemia SR Leukemia A549/ATCC Non-Small Cell Lung EKVXNon-Small Cell Lung HOP-62 Non-Small Cell Lung HOP-92 Non-Small CellLung NCI-H226 Non-Small Cell Lung NCI-H23 Non-Small Cell Lung NCI-H322MNon-Small Cell Lung NCI-H460 Non-Small Cell Lung NCI-H522 Non-Small CellLung COLO 205 Colon HCC-2998 Colon HCT-116 Colon HCT-15 Colon HT29 ColonKM12 Colon SW-620 Colon SF-268 CNS SF-295 CNS SF-539 CNS SNB-19 CNSSNB-75 CNS U251 CNS LOX IMVI Melanoma MALME-3M Melanoma M14 MelanomaSK-MEL-2 Melanoma SK-MEL- 28 Melanoma SK-MEL-5 Melanoma UACC-257Melanoma UACC-62 Melanoma IGR-OV1 Ovarian OVCAR-3 Ovarian OVCAR-4Ovarian OVCAR-5 Ovarian OVCAR-8 Ovarian SK-OV-3 Ovarian 786-0 Renal A498Renal ACHN Renal CAKI-1 Renal RXF 393 Renal SN12C Renal TK-10 RenalUO-31 Renal PC-3 Prostate DU-145 Prostate MCF7 Breast NCI/ADR-RES BreastMDA-MB-231/ATCC Breast HS 578T Breast MDA-MB-435 Breast MDA-N BreastBT-549 Breast T-47D Breast

Lysates were normalized for total protein content prior to analysis.Relative levels of TUCAN protein varied widely among the tumor linestested, with some cell lines containing especially abundant levels ofthis protein (for example, MCF7 breast cancer cells, OVCAR5 ovariancancer cells, and NCI-H322M lung cancer cells). TUCAN protein also waspresent in HL-60 leukemia cells, SNB-19 CNS cancer cells, MDA-MB-231breast cancer cells, IGROV1 ovarian cancer cells, NCI-H226 non-smallcell lung cancer cells, NCI-H23 non small cell lung cancer cells, M14melanoma cells, Du-145 prostate cancer cells, UO-31 renal cancer cells,and K562 leukemia cells. In some of these tumor lines, TUCAN migrated inSDS-PAGE as a broad band or as an apparent doublet, indicating thatmultiple forms of TUCAN protein can be present in cancer cells (FIG.6A).

The levels of endogenous TUCAN protein in some of these cancer celllines were compared with the transfected HEK293T and Jurkat cells. Thelevels of plasmid-derived TUCAN produced in transiently transfectedHEK293T cells were comparable to the endogenous levels of TUCAN found inMCF7 breast cancer cells (FIG. 6B). Levels of plasmid-derived TUCANproduced in the stably transfected Jurkat cells were comparable inamount to endogenous TUCAN measured in OVCAR5 ovarian and NCI-H322M lungcancer cell lines.

In summary, TUCAN is expressed in a variety of tumor cell lines,including cancer cells obtained from human breast, ovarian, lung, CNS,leukemia, kidney, prostate, skin and colon tumors.

EXAMPLE VIII Elevated TUCAN Expression in Colon Cancers Correlates withReduced Patient Survival

This example shows that TUCAN expression is elevated in colon cancersand that TUCAN elevation correlates with reduced colon cancer patientsurvival.

Using anti-TUCAN antibodies, the expression of TUCAN protein wasanalyzed by immunohistochemical methods in a collection of 102 archivalparaffin-embedded colon cancer specimens derived from patients withuniform clinical stage (Duke's B; Stage II) and treatment (surgerywithout adjuvant chemotherapy). A tissue microarray was constructed sothat all 102 tumor specimens could be analyzed on a single glass slide,thus minimizing differences in immuno-intensity due to technicalartifacts (FIG. 7).

Normal human tissues for immunohistochemistry analysis were obtainedfrom biopsy and autopsy specimens, fixed in Bouin's solution (Sigma),and embedded in paraffin. Colon carcinoma specimens were obtained fromDepartment of Pathology, Yonsei University, College of Medicine, Seoul,Korea. Tissue samples included 102 primary tumors derived from patientswho presented between 1986 and 1996 with stage II disease (Duke'sB-stage), as defined by American Joint Committee on Cancer and UnionInternationale Contre le Cancer (AJCC/UICC) criteria. All patients weretreated by surgical resection of the involved segment of colon. Nopostoperative adjuvant chemotherapy was performed initially in allcases. However, chemotherapy was administered for some patients afterrelapse. Clinical data represent a median follow up of 60 months.

To construct colon cancer microarrays, 2-5 cylinders of 1 mm diametertissue were taken from representative areas of archival paraffin blockscontaining 8% formalin-fixed tumor and arrayed into a new recipientparaffin block with a custom-built precision instrument (BeecherInstruments, Silver Spring, Md.). Serial sections (4 um) were applied to3-aminopropyltri-ethoxysilane (APES)-coated slides (Sigma), as describedin Rentrop et al. Histochem J. 18:271-276 (1986).

For immunohistochemistry, dewaxed tissue sections were immunostainedusing a diaminobenzidine (DAB)-based detection method, employing theEnvision-Plus-Horse Radish Peroxidase (HRP) system (DAKO) using anautomated immunostainer (Dako Universal Staining System) (Krajewski etal. Proc. Natl. Acad. Sci. USA 96:5752-5757 (1999)). Anti-TUCAN antibodywas applied at 1:5000 (v/v). Incubation with antiserum preabsorbed with5 ug/ml of either synthetic peptide (BUR215) or recombinantGST-CARD/TUCAN protein (BUR206) immunogen was used to verify specificityof the results. The scoring of tumor immunostaining was based on thepercentage of immunopositive cells (0-100) multiplied by stainingintensity score (0/1/2/3), yielding scores of 0-300.

Data were analyzed using the JMP Statistics software package (SASInstitute). An unpaired t-test method and Pearson ChiSquare test wereused for correlation of TUCAN immunostaining data with the patientsurvival.

Of the 102 tumor specimens arrayed, 66 contained adjacent normal colonicepithelium in the same section, permitting comparisons of the intensityof TUCAN immunostaining in tumor versus normal cells. TUCANimmuno-intensity was stronger in the invasive cancer cells compared tonormal colonic epithelial cells in 42 of 66 (64%) of these specimens,indicating that roughly two-thirds of colon cancers have up-regulatedlevels of TUCAN protein. TUCAN immunoreactivity was present diffuselythrough the cytosol of these cells (FIG. 7). Control staining performedwith either preimmune serum or with anti-TUCAN antiserum that had beenpreabsorbed with TUCAN immunogen confirmed that these results werespecific for anti-TUCAN.

Tumor immunostaining results were scored with respect toimmuno-intensity (ranked on a scale of 0-3), percentage immunopositivity(0-100%), and immunoscore (which is the product of immuno-intensity andimmuno-percentage), and these data were correlated with patient survivalinformation. TUCAN immunostaining was significantly higher amongpatients who died of their cancer (n=31), compared to patients whoremained alive without disease (n=61) or alive with recurrent disease(n=10). A summary of TUCAN immunostaining results is shown below inTable 5. TABLE 5 Summary of TUCAN Immunostaining Results Patient %Immunopositivity Immunointensity Immunoscore Status n Mean ± SE MedianMean ± SE Median Mean ± SE Median Alive 61 58 ± 3 60 1.4 ± 0.1 1 92 ± 980 without disease Alive with 10 54 ± 7 55 1.3 ± 0.2 1  73 ± 21 65disease Dead from 31 90 ± 4 95 2.5 ± 0.1 3 224 ± 12 240 disease p-values102 p < .0001 p < .0001 p < .0001

In summary, TUCAN expression is abnormally elevated in a substantialproportion of early-stage colon cancers. Furthermore, elevated TUCANexpression correlates with reduced patient survival.

EXAMPLE IX TUCAN Binds Selectively to Pro-Caspase-9 and to Itself

This example shows that TUCAN binds selectively to Pro-Caspase-9 and toitself. Since CARDS are known for their ability to bind each other,TUCAN was tested for interactions with the CARD-containing proteinspro-Caspase-1, pro-Caspase-2, pro-Caspase-9, Apaf1, Nod1 (CARD4), CED4,NAC (DEFCAP), Cardiak (RIP2), Raidd (CRADD), Bcl10 (CIPER; huE10),cIAP1, cIAP2, CLAN, CARD9, and itself. Among these, TUCAN associatedonly with pro-Caspase-9 and itself.

FIG. 8 shows representative results from co-immunoprecipitationexperiments performed using TUCAN containing either Flag or Myc epitopetags. The TUCAN polypeptides were expressed by transient transfection inHEK293T cells together with epitope-tagged pro-caspase-9 or otherproteins. An inactive mutant of pro-caspase-9 in which the catalyticcysteine was substitute with alanine (Cys287/Ala) was employed for theseexperiments to avoid induction of apoptosis (Cardone et al. Science282:1318-1321 (1998)). Cell lysates were prepared from transfected cellsand immunoprecipitations were performed using anti-Flag or anti-Mycantibodies, followed by SDS-PAGE/immunoblot analysis. Representativeresults are presented in FIG. 8A, which shows that TUCANco-immunoprecipitated with pro-Caspase-9 but not the CARD-containingprotein Apaf1. TUCAN also did not co-immunoprecipitate with theCARD-containing proteins pro-Caspase-1, pro-Caspase-2, Nod1, CED4, NAC,Cardiak, Raidd, Bcl10, CLAN, CARD9, cIAP1, and cIAP2. Moreover, TUCANdid not associate non-specifically with caspases, asco-immunoprecipitation experiments did not reveal interactions with theDED-containing caspases, pro-caspase-8 and -10 (FIG. 8A).

To determine the role of the CARD domain within TUCAN for interactionswith pro-Caspase-9, fragments of TUCAN were expressed. The TUCANfragments contained essentially only the CARD (residues 345-431) orlacked the CARD (residues 1-337) (ΔCARD). Pro-Caspase-9co-immunoprecipitated with full-length TUCAN and the CARD only fragmentbut not the ΔCARD fragment of TUCAN (FIG. 8B). Thus, the CARD domain ofTUCAN is necessary and sufficient for association with pro-Caspase-9.Self-association of TUCAN was also confirmed by co-immunoprecipitationexperiments, using HA and Myc-tagged proteins and contrasting thefull-length, CARD-only, and ΔCARD proteins. Full-length TUCAN interactedwith full-length TUCAN and the CARD-only fragment but not the ΔCARDfragment (FIG. 8C). Thus, the CARD domain of TUCAN is necessary andsufficient for self-association.

Throughout this application various publications have been referencedwithin parentheses. The disclosures of these publications in theirentireties are hereby incorporated by reference in this application inorder to more fully describe the state of the art to which thisinvention pertains.

Although the invention has been described with reference to thedisclosed embodiments, those skilled in the art will readily appreciatethat the specific experiments detailed are only illustrative of theinvention. It should be understood that various modifications can bemade without departing from the spirit of the invention.

1. A method for determining a prognosis for survival for a cancerpatient, comprising: (a) measuring a level of a TUCAN in a neoplasticcell-containing sample from said cancer patient, and (b) comparing thelevel of TUCAN in said sample to a reference level of TUCAN, wherein alow level of TUCAN in said sample correlates with increased survival ofsaid patient.
 2. The method of claim 1, wherein said survival is overallsurvival.
 3. The method of claim 1, wherein said survival isdisease-free survival.
 4. The method of claim 1, wherein said cancerpatient has a cancer selected from the group consisting of: coloncancer, gastrointestinal cancer, breast cancer, ovarian cancer, lungcancer, leukemia, CNS cancer, melanoma, prostate cancer, and renalcancer.
 5. The method of claim 1, wherein said sample is colon tumortissue.
 6. The method of claim 1, wherein said sample is a fluidselected from the group consisting of blood, serum, urine, semen andstool. 7-8. (canceled)
 9. The method of claim 1, wherein a level of aTUCAN nucleic acid is measured.
 10. The method of claim 1, wherein saidpatient has an early stage of cancer.
 11. The method of claim 1, whereinsaid level of TUCAN is used to determine if said patient is at risk forrelapse.
 12. The method of claim 1, wherein said level of TUCAN is usedto determine a proper course of treatment for said patient.
 13. A methodof determining a prognosis for survival for a cancer patient,comprising: (a) measuring levels of TUCAN and one or more biomarkersselected from the group consisting of cIAP2, Apaf1, Bcl-2 and Smac in aneoplastic cell-containing sample from said cancer patient, and (b)comparing the level of TUCAN and the one or more selected biomarkers insaid sample to a reference level of TUCAN and said one more selectedbiomarkers, wherein a low level of TUCAN and a high level of any ofApaf1, Bcl-2 or Smac, or a low level of TUCAN and a low level of cIAP2,in said sample correlate with increased survival of said patient. 14.The method of claim 13, wherein said survival is overall survival. 15.The method of claim 13, wherein said survival is disease-free survival.16. The method of claim 13, wherein cIAP2 is a selected biomarker. 17.The method of claim 13, wherein Apaf1 is a selected biomarker.
 18. Themethod of claim 13, wherein Bcl-2 is a selected biomarker.
 19. Themethod of claim 13 wherein Smac is a selected biomarker.
 20. The methodof claim 13, wherein said cancer patient has a cancer selected from thegroup consisting of: colon cancer, gastrointestinal cancer, breastcancer, ovarian cancer, lung cancer, leukemia, CNS cancer, melanoma,prostate cancer, and renal cancer.
 21. The method of claim 13, whereinsaid sample is colon tumor tissue.
 22. The method of claim 13, whereinsaid sample is a fluid selected from the group consisting of blood,serum, semen, urine, and stool. 23-24. (canceled)
 25. The method ofclaim 13, wherein a level of TUCAN or a biomarker nucleic acid ismeasured.
 26. The method of claim 13, wherein said patient has an earlystage of cancer.
 27. The method of claim 13, wherein the levels of TUCANand one or more biomarkers are used to determine if said patient is atrisk for relapse.
 28. The method of claim 13, wherein the levels ofTUCAN and one or more biomarkers are used to determine a proper courseof treatment for said patient.
 29. The method of claim 13, furthercomprising selecting two or more biomarkers from the group consisting ofcIAP2, Apaf1, Bcl-2 and Smac.
 30. A method for monitoring theeffectiveness of a course of treatment for a patient with cancer,comprising: (a) determining a level of a TUCAN in a neoplasticcell-containing sample from a cancer patient prior to treatment, and (b)determining the level of TUCAN in a neoplastic cell-containing samplefrom said patient after treatment, whereby comparison of said TUCANlevel prior to treatment with the TUCAN level after treatment indicatesthe effectiveness of said treatment.
 31. The method of claim 30, whereinsaid cancer patient has a cancer selected from the group consisting of:colon cancer, gastrointestinal cancer, breast cancer, ovarian cancer,lung cancer, leukemia, CNS cancer, melanoma, prostate cancer, and renalcancer.
 32. The method of claim 30, wherein said sample is colon tumortissue.
 33. The method of claim 30, wherein said sample is a fluidselected from the group consisting of blood, serum, urine, semen andstool. 34-35. (canceled)
 36. The method of claim 30, wherein a level ofa TUCAN nucleic acid is measured.
 37. The method of claim 30, whereinsaid patient has an early stage of cancer.
 38. The method of claim 30,further comprising determining a level of a biomarker selected from thegroup consisting of cIAP2, Apaf1, Smac and Bcl-2 in said neoplasticcell-containing sample from said cancer patient prior to and aftertreatment, wherein the levels of the selected biomarker and TUCAN priorto treatment are compared with the levels of the selected biomarker andTUCAN after treatment to indicate the effectiveness of said treatment.39. A method of determining a prognosis for survival for a cancerpatient, comprising: (a) measuring a level of TUCAN in a neoplasticcell-containing sample from said cancer patient, and (b) classifyingsaid patient as belonging to either a first or second group of patients,wherein said first group of patients having low levels of TUCAN isclassified as having an increased likelihood of survival compared tosaid second group of patients having high levels of TUCAN.
 40. Themethod of claim 39, wherein said survival is overall survival.
 41. Themethod of claim 39, wherein said survival is disease-free survival. 42.The method of claim 39, wherein said cancer patient has a cancerselected from the group consisting of: colon cancer, gastrointestinalcancer, breast cancer, ovarian cancer, lung cancer, leukemia, braincancer, melanoma, prostate cancer, and renal cancer.
 43. The method ofclaim 39, wherein said sample is colon tumor tissue.
 44. The method ofclaim 39, wherein said sample is a fluid selected from the groupconsisting of blood, serum, urine, semen and stool. 45-46. (canceled)47. The method of claim 39, wherein a level of TUCAN nucleic acid ismeasured.
 48. The method of claim 39, wherein said patient has an earlystage of cancer.
 49. The method of claim 39, further comprising: (a)determining a level of cIAP2 said neoplastic cell-containing sample fromsaid cancer patient, and (b) classifying said patient as belonging toeither a first or second group of patient, wherein said first group ofpatients having low levels of TUCAN and low levels of cIAP2 isclassified as having increased likelihood of survival compared to saidsecond group of patients having high levels of TUCAN and high levels ofcIAP2.
 50. The method of claim 39, further comprising: (a) determining alevel of a biomarker selected from the group consisting of Apaf1, Smacand Bcl-2 in said neoplastic cell-containing sample from said cancerpatient, and (b) classifying said patient as belonging to either a firstor second group of patient, wherein said first group of patients havinglow levels of TUCAN and high levels of any of Apaf1, Smac or Bcl-2 isclassified as having increased likelihood of survival compared to saidsecond group of patients having high levels of TUCAN and low levels ofany of Apaf1, Smac or Bcl-2.